Abstract Transient rendering simulates light in motion, measuring the time of flight from the light source to the camera. However, the stochastic nature of Monte Carlo is aggravated in transient rendering, since samples are now spread along the temporal domain. In our work, we propose to denoise transient Monte Carlo renders by exploiting the spatio‐temporal correlation of transient light transport, extending a recent statistical denoising formulation. By relying on statistics, we achieve a near‐optimal tradeoff between reduced variance and introduced bias. We efficiently collect per‐time‐bin statistics in the temporal domain while avoiding impractical memory requirements, and use these collected statistics to analyze the spatio‐temporal correlation and discriminate which time bins should be combined. Our statistics‐based transient denoiser does not hallucinate, guarantees convergence of the result, is efficient, does not require any training and naturally handles participating media. We believe that the generality of our method might pave the way for denoising time‐resolved Monte Carlo simulations in other domains, such as non‐line‐of‐sight imaging, acoustic rendering, or absorption microscopy.

Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that allows precise control over the camera when generating both photographic and artistic images. Unlike prior methods that rely on predefined shots, we rely solely on four simple extrinsic and intrinsic camera parameters, removing the need for pre-existing geometry, reference 3D objects, and multi-view data. We also present a novel dataset with more than 57,000 images, along with their text prompts and ground-truth camera parameters. Our evaluation shows precise camera control in text-to-image generation, surpassing traditional prompt engineering approaches.

Non-line-of-sight imaging employs ultra-fast illumination and sensing devices to reconstruct scenes outside their line of sight by analyzing the temporal profile of indirect scattered illumination on a secondary relay surface. Commonly, the NLOS methods transform the temporal domain into the frequency domain and operate on it, and then identify surface locations by locating the maxima in amplitude along the reconstruction volume. Phase information, which is virtual as it results from a Fourier transform, is very often discarded or ignored. We incorporate phase information into our novel Zero-Phase Phasor Fields imaging technique, which we derive for a confocal capture configuration. We show how, at positions that belong to the hidden geometry, we can ensure the phase is zero, so we can locate the hidden geometry with great precision by locating the zero crossings in the phase. This allows us to reconstruct at widely spaced locations and still achieve up to 125 micrometer depth precision, as our experimental validation shows with both synthetic and captured data, the latter publicly available. Moreover, the phase is robust to noise, as we demonstrate with decreasing signal-to-noise ratio using publicly available dataset captures of the same scene.

Virtual reality (VR) experiences often leverage rich and spatialized multimodal environments to increase immersion and engagement. This demands a consistent spatial perception of audiovisual stimuli, since perceived discrepancies can disrupt the sense of presence. In this work, we investigate the consequences of two types of spatial audiovisual disparities: true disparity, where there is a measurable spatial offset between auditory and visual cues, and perceptual disparity, where users report misalignment despite cues being colocated. Unlike most previous studies that employed controlled but simplified experimental setups, our research focuses on complex, realistic VR environments, allowing us to assess the actual implications for VR content design. Our experiments indicate that users are highly sensitive to true audiovisual disparities in controlled environments, detecting even minor misalignments. However, when engaged in additional tasks within realistic settings, their ability to notice such discrepancies diminishes significantly. We also observed that previously found perceptual disparities persist in complex audiovisual environments. However, we identify self-initiated head rotations as a key factor; its absence prevents the effect entirely. We hope our findings offer practical insights for designing more immersive and perceptually coherent VR experiences.

Abstract Large diffusion models have made a remarkable leap synthesizing high‐quality artistic images from text descriptions. However, these powerful pre‐trained models still lack control to guide key material appearance properties, such as gloss. In this work, we present a threefold contribution: (1) we analyze how gloss is perceived across different artistic styles (i.e., oil painting, watercolor, ink pen, charcoal, and soft crayon); (2) we leverage our findings to create a dataset with 1,336,272 stylized images of many different geometries in all five styles, including automatically‐computed text descriptions of their appearance (e.g., “A glossy bunny hand painted with an orange soft crayon”); and (3) we train ControlNet to condition Stable Diffusion XL synthesizing novel painterly depictions of new objects, using simple inputs such as edge maps, hand‐drawn sketches, or clip arts. Compared to previous approaches, our framework yields more accurate results despite the simplified input, as we show both quantitative and qualitatively.

Selection is the first step in many image editing processes, enabling faster and simpler modifications of all pixels sharing a common modality. In this work, we present a method for material selection in images, robust to lighting and reflectance variations, which can be used for downstream editing tasks. We rely on vision transformer (ViT) models and leverage their features for selection, proposing a multi-resolution processing strategy that yields finer and more stable selection results than prior methods. Furthermore, we enable selection at two levels: texture and subtexture, leveraging a new two-level material selection (DuMaS) dataset which includes dense annotations for over 800,000 synthetic images, both on the texture and subtexture levels.

introducing a novel inversion method that leverages directionality information of polarized measurements to reduce directional ambiguities in the reconstruction.Our method is capable of imaging features of hidden surfaces inside the missing cone space of state-of-the-art NLOS methods, yielding fine reconstruction details even when using a fraction of measured points on the relay surface.We demonstrate the benefits of our method in both simulated and experimental scenarios.

Predicting the path followed by the viewer’s eyes when observing an image (a scanpath) is a challenging problem, particularly due to the inter- and intra-observer variability and the spatio-temporal dependencies of the visual attention process. Most existing approaches have focused on progressively optimizing the prediction of a gaze point given the previous ones. In this work we propose instead a probabilistic approach, which we call tSPM-Net. We build our method to account for observers’ variability by resorting to Bayesian deep learning and a probabilistic approach. Besides, we optimize our model to jointly consider both spatial and temporal dimensions of scanpaths using a novel spatio-temporal loss function based on a combination of Kullback–Leibler divergence and dynamic time warping. Our tSPM-Net yields results that outperform those of current state-of-the-art approaches, and are closer to the human baseline, suggesting that our model is able to generate scanpaths whose behavior closely resembles those of the real ones.

Abstract Estimating perceptual attributes of materials directly from images is a challenging task due to their complex, not fully‐understood interactions with external factors, such as geometry and lighting. Supervised deep learning models have recently been shown to outperform traditional approaches, but rely on large datasets of human‐annotated images for accurate perception predictions. Obtaining reliable annotations is a costly endeavor, aggravated by the limited ability of these models to generalise to different aspects of appearance. In this work, we show how a much smaller set of human annotations (“strong labels”) can be effectively augmented with automatically derived “weak labels” in the context of learning a low‐dimensional image‐computable gloss metric. We evaluate three alternative weak labels for predicting human gloss perception from limited annotated data. Incorporating weak labels enhances our gloss prediction beyond the current state of the art. Moreover, it enables a substantial reduction in human annotation costs without sacrificing accuracy, whether working with rendered images or real photographs.

Abstract The light field in an underwater environment is characterized by complex multiple scattering interactions and wavelength‐dependent attenuation, requiring significant computational resources for the simulation of underwater scenes. We present a novel approach that makes it possible to simulate multi‐spectral underwater scenes, in a physically‐based manner, in real time. Our key observation is the following: In the vertical direction, the steady decay in irradiance as a function of depth is characterized by the diffuse downwelling attenuation coefficient, which oceanographers routinely measure for different types of waters. We rely on a database of such real‐world measurements to obtain an analytical approximation to the Radiative Transfer Equation, allowing for real‐time spectral rendering with results comparable to Monte Carlo ground‐truth references, in a fraction of the time. We show results simulating underwater appearance for the different optical water types, including volumetric shadows and dynamic, spatially varying lighting near the water surface.

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.

Humans perceive the world by integrating multimodal sensory feedback, including visual and auditory stimuli, which holds true in virtual reality (VR) environments. Proper synchronization of these stimuli is crucial for perceiving a coherent and immersive VR experience. In this work, we focus on the interplay between audio and vision during localization tasks involving natural head-body rotations. We explore the impact of audio-visual offsets and rotation velocities on users' directional localization acuity for various viewing modes. Using psychometric functions, we model perceptual disparities between visual and auditory cues and determine offset detection thresholds. Our findings reveal that target localization accuracy is affected by perceptual audio-visual disparities during head-body rotations, but remains consistent in the absence of stimuli-head relative motion. We then showcase the effectiveness of our approach in predicting and enhancing users' localization accuracy within realistic VR gaming applications. To provide additional support for our findings, we implement a natural VR game wherein we apply a compensatory audio-visual offset derived from our measured psychometric functions. As a result, we demonstrate a substantial improvement of up to 40% in participants' target localization accuracy. We additionally provide guidelines for content creation to ensure coherent and seamless VR experiences.

The recently introduced Phasor Fields framework for non-line-of-sight imaging allows to image hidden scenes by treating a relay surface as a virtual camera. It formulates the problem as a diffractive wave propagation, solved by the Rayleigh–Sommerfeld diffraction (RSD) integral. Efficient Phasor Fields implementations employ RSD-based kernels to propagate waves from parallel planes by means of 2D convolutions. However, the kernel storage requisites are prohibitive, hampering the integration of these techniques in memory-constrained devices and applications like car safety.Instead of relying on expensive RSD kernels, we propose the use of alternative virtual lenses to focus on the incoming phasor field and image hidden scenes. In particular, we propose using zone plates (ZP), which require significant less memory. As our results show, our ZP virtual lenses allow us to obtain reasonable reconstructions of the hidden scene, offering an attractive trade-off for memory-constrained devices.

Existing time-resolved non-line-of-sight (NLOS) imaging methods reconstruct hidden scenes by inverting the optical paths of indirect illumination measured at visible relay surfaces. These methods are prone to reconstruction artifacts due to inversion ambiguities and capture noise, which are typically mitigated through the manual selection of filtering functions and parameters. We introduce a fully-differentiable end-to-end NLOS inverse rendering pipeline that self-calibrates the imaging parameters during the reconstruction of hidden scenes, using as input only the measured illumination while working both in the time and frequency domains. Our pipeline extracts a geometric representation of the hidden scene from NLOS volumetric intensities and estimates the time-resolved illumination at the relay wall produced by such geometric information using differentiable transient rendering. We then use gradient descent to optimize imaging parameters by minimizing the error between our simulated time-resolved illumination and the measured illumination. Our end-to-end differentiable pipeline couples diffraction-based volumetric NLOS reconstruction with path-space light transport and a simple ray marching technique to extract detailed, dense sets of surface points and normals of hidden scenes.We demonstrate the robustness of our method to consistently reconstruct geometry and albedo, even under significant noise levels.

We introduce text2fabric, a novel dataset that links free-text descriptions to various fabric materials. The dataset comprises 15,000 natural language descriptions associated to 3,000 corresponding images of fabric materials. Traditionally, material descriptions come in the form of tags/keywords, which limits their expressivity, induces pre-existing knowledge of the appropriate vocabulary, and ultimately leads to a chopped description system. Therefore, we study the use of free-text as a more appropriate way to describe material appearance, taking the use case of fabrics as a common item that non-experts may often deal with. Based on the analysis of the dataset, we identify a compact lexicon, set of attributes and key structure that emerge from the descriptions. This allows us to accurately understand how people describe fabrics and draw directions for generalization to other types of materials. We also show that our dataset enables specializing large vision-language models such as CLIP, creating a meaningful latent space for fabric appearance, and significantly improving applications such as fine-grained material retrieval and automatic captioning.

Non-line-of-sight (NLOS) imaging methods are capable of reconstructing complex scenes that are not visible to an observer using indirect illumination. However, they assume only third-bounce illumination, so they are currently limited to single-corner configurations, and present limited visibility when imaging surfaces at certain orientations. To reason about and tackle these limitations, we make the key observation that planar diffuse surfaces behave specularly at wavelengths used in the computational wave-based NLOS imaging domain. We call such surfaces virtual mirrors. We leverage this observation to expand the capabilities of NLOS imaging using illumination beyond the third bounce, addressing two problems: imaging single-corner objects at limited visibility angles, and imaging objects hidden behind two corners. To image objects at limited visibility angles, we first analyze the reflections of the known illuminated point on surfaces of the scene as an estimator of the position and orientation of objects with limited visibility. We then image those limited visibility objects by computationally building secondary apertures at other surfaces that observe the target object from a direct visibility perspective. Beyond single-corner NLOS imaging, we exploit the specular behavior of virtual mirrors to image objects hidden behind a second corner by imaging the space behind such virtual mirrors, where the mirror image of objects hidden around two corners is formed. No specular surfaces were involved in the making of this paper.

Understanding human visual behavior within virtual reality environments is crucial to fully leverage their potential. While previous research has provided rich visual data from human observers, existing gaze datasets often suffer from the absence of multimodal stimuli. Moreover, no dataset has yet gathered eye gaze trajectories (i.e., scanpaths) for dynamic content with directional ambisonic sound, which is a critical aspect of sound perception by humans. To address this gap, we introduce D-SAV360, a dataset of 4,609 head and eye scanpaths for 360° videos with first-order ambisonics. This dataset enables a more comprehensive study of multimodal interaction on visual behavior in virtual reality environments. We analyze our collected scanpaths from a total of 87 participants viewing 85 different videos and show that various factors such as viewing mode, content type, and gender significantly impact eye movement statistics. We demonstrate the potential of D-SAV360 as a benchmarking resource for state-of-the-art attention prediction models and discuss its possible applications in further research. By providing a comprehensive dataset of eye movement data for dynamic, multimodal virtual environments, our work can facilitate future investigations of visual behavior and attention in virtual reality.

Visual behavior depends on both bottom-up mechanisms, where gaze is driven by the visual conspicuity of the stimuli, and top-down mechanisms, guiding attention towards relevant areas based on the task or goal of the viewer. While this is well-known, visual attention models often focus on bottom-up mechanisms. Existing works have analyzed the effect of high-level cognitive tasks like memory or visual search on visual behavior; however, they have often done so with different stimuli, methodology, metrics and participants, which makes drawing conclusions and comparisons between tasks particularly difficult. In this work we present a systematic study of how different cognitive tasks affect visual behavior in a novel within-subjects design scheme. Participants performed free exploration, memory and visual search tasks in three different scenes while their eye and head movements were being recorded. We found significant, consistent differences between tasks in the distributions of fixations, saccades and head movements. Our findings can provide insights for practitioners and content creators designing task-oriented immersive applications.

Human performance is poor at detecting certain changes in a scene, a phenomenon known as change blindness. Although the exact reasons of this effect are not yet completely understood, there is a consensus that it is due to our constrained attention and memory capacity: We create our own mental, structured representation of what surrounds us, but such representation is limited and imprecise. Previous efforts investigating this effect have focused on 2D images; however, there are significant differences regarding attention and memory between 2D images and the viewing conditions of daily life. In this work, we present a systematic study of change blindness using immersive 3D environments, which offer more natural viewing conditions closer to our daily visual experience. We devise two experiments; first, we focus on analyzing how different change properties (namely type, distance, complexity, and field of view) may affect change blindness. We then further explore its relation with the capacity of our visual working memory and conduct a second experiment analyzing the influence of the number of changes. Besides gaining a deeper understanding of the change blindness effect, our results may be leveraged in several VR applications such as redirected walking, games, or even studies on saliency or attention prediction.

Virtual reality (VR) has the potential to change the way people consume content, and has been predicted to become the next big computing paradigm. However, much remains unknown about the grammar and visual language of this new medium, and understanding and predicting how humans behave in virtual environments remains an open problem. In this work, we propose a novel saliency prediction model which exploits the joint potential of spherical convolutions and recurrent neural networks to extract and model the inherent spatio-temporal features from 360° videos. We employ Convolutional Long Short-Term Memory cells (ConvLSTMs) to account for temporal information at the time of feature extraction rather than to post-process spatial features as in previous works. To facilitate spatio-temporal learning, we provide the network with an estimation of the optical flow between 360° frames, since motion is known to be a highly salient feature in dynamic content. Our model is trained with a novel spherical Kullback–Leibler Divergence (KLDiv) loss function specifically tailored for saliency prediction in 360° content. Our approach outperforms previous state-of-the-art works, being able to mimic human visual attention when exploring dynamic 360° videos.

Abstract Single‐image appearance editing is a challenging task, traditionally requiring the estimation of additional scene properties such as geometry or illumination. Moreover, the exact interaction of light, shape and material reflectance that elicits a given perceptual impression is still not well understood. We present an image‐based editing method that allows to modify the material appearance of an object by increasing or decreasing high‐level perceptual attributes, using a single image as input. Our framework relies on a two‐step generative network, where the first step drives the change in appearance and the second produces an image with high‐frequency details. For training, we augment an existing material appearance dataset with perceptual judgements of high‐level attributes, collected through crowd‐sourced experiments, and build upon training strategies that circumvent the cumbersome need for original‐edited image pairs. We demonstrate the editing capabilities of our framework on a variety of inputs, both synthetic and real, using two common perceptual attributes (Glossy and Metallic), and validate the perception of appearance in our edited images through a user study.

Abstract Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state‐of‐the‐art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surround‐aware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone‐mapping and improved accuracy in visual difference prediction.

Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent techniques allow to capture polarimetric spatially-varying reflectance information, but limited to a single view, or to cover all view directions, but limited to spherical objects made of a single homogeneous material. We present sparse ellipsometry , a portable polarimetric acquisition method that captures both polarimetric SVBRDF and 3D shape simultaneously. Our handheld device consists of off-the-shelf, fixed optical components. Instead of days, the total acquisition time varies between twenty and thirty minutes per object. We develop a complete polarimetric SVBRDF model that includes diffuse and specular components, as well as single scattering, and devise a novel polarimetric inverse rendering algorithm with data augmentation of specular reflection samples via generative modeling. Our results show a strong agreement with a recent ground-truth dataset of captured polarimetric BRDFs of real-world objects.

The light transport matrix (LTM) is an instrumental tool in line-of-sight (LOS) imaging, describing how light interacts with the scene and enabling applications such as relighting or separation of illumination components. We introduce a framework to estimate the LTM of non-line-of-sight (NLOS) scenarios, coupling recent virtual forward light propagation models for NLOS imaging with the LOS light transport equation. We design computational projector-camera setups, and use these virtual imaging systems to estimate the transport matrix of hidden scenes. We introduce the specific illumination functions to compute the different elements of the matrix, overcoming the challenging wide-aperture conditions of NLOS setups. Our NLOS light transport matrix allows us to (re)illuminate specific locations of a hidden scene, and separate direct, first-order indirect, and higher-order indirect illumination of complex cluttered hidden scenes, similar to existing LOS techniques.

Recent differentiable rendering techniques have become key tools to tackle many inverse problems in graphics and vision. Existing models, however, assume steady-state light transport, i.e., infinite speed of light. While this is a safe assumption for many applications, recent advances in ultrafast imaging leverage the wealth of information that can be extracted from the exact time of flight of light. In this context, physically-based transient rendering allows to efficiently simulate and analyze light transport considering that the speed of light is indeed finite. In this paper, we introduce a novel differentiable transient rendering framework, to help bring the potential of differentiable approaches into the transient regime. To differentiate the transient path integral we need to take into account that scattering events at path vertices are no longer independent; instead, tracking the time of flight of light requires treating such scattering events at path vertices jointly as a multidimensional, evolving manifold. We thus turn to the generalized transport theorem, and introduce a novel correlated importance term, which links the time-integrated contribution of a path to its light throughput, and allows us to handle discontinuities in the light and sensor functions. Last, we present results in several challenging scenarios where the time of flight of light plays an important role such as optimizing indices of refraction, non-line-of-sight tracking with nonplanar relay walls, and non-line-of-sight tracking around two corners.

Non-line-of-sight (NLOS) imaging aims to visualize occluded scenes by exploiting indirect reflections on visible surfaces. Previous methods approach this problem by inverting the light transport on the hidden scene, but are limited to isolated, diffuse objects. The recently introduced phasor fields framework computationally poses NLOS reconstruction as a virtual line-of-sight (LOS) problem, lifting most assumptions about the hidden scene. In this work we complement recent theoretical analysis of phasor field-based reconstruction, by empirically analyzing the effect of reflectance of the hidden scenes on reconstruction. We experimentally study the reconstruction of hidden scenes composed of objects with increasingly specular materials. Then, we evaluate the effect of the virtual aperture size on the reconstruction, and establish connections between the effect of these two different dimensions on the results. We hope our analysis helps to characterize the imaging capabilities of this promising new framework, and foster new NLOS imaging modalities.

Virtual Reality (VR) has grown since the first devices for personal use became available on the market. However, the production of cinematographic content in this new medium is still in an early exploratory phase. The main reason is that cinematographic language in VR is still under development, and we still need to learn how to tell stories effectively. A key element in traditional film editing is the use of different cutting techniques, in order to transition seamlessly from one sequence to another. A fundamental aspect of these techniques is the placement and control over the camera. However, VR content creators do not have full control of the camera. Instead, users in VR can freely explore the 360° of the scene around them, which potentially leads to very different experiences. While this is desirable in certain applications such as VR games, it may hinder the experience in narrative VR. In this work, we perform a systematic analysis of users’ viewing behavior across cut boundaries while watching professionally edited, narrative 360° videos. We extend previous metrics for quantifying user behavior in order to support more complex and realistic footage, and we introduce two new metrics that allow us to measure users’ exploration in a variety of different complex scenarios. From this analysis, (i) we confirm that previous insights derived for simple content hold for professionally edited content, and (ii) we derive new insights that could potentially influence VR content creation, informing creators about the impact of different cuts in the audience’s behavior.

The unique and visually mesmerizing appearance of pearlescent materials has made them an indispensable ingredient in a diverse array of applications including packaging, ceramics, printing, and cosmetics. In contrast to their natural counterparts, such synthetic examples of pearlescence are created by dispersing microscopic interference pigments within a dielectric resin. The resulting space of materials comprises an enormous range of different phenomena ranging from smooth lustrous appearance reminiscent of pearl to highly directional metallic gloss, along with a gradual change in color that depends on the angle of observation and illumination. All of these properties arise due to a complex optical process involving multiple scattering from platelets characterized by wave-optical interference. This article introduces a flexible model for simulating the optics of such pearlescent 3D microstructures. Following a thorough review of the properties of currently used pigments and manufacturing-related effects that influence pearlescence, we propose a new model which expands the range of appearance that can be represented, and closely reproduces the behavior of measured materials, as we show in our comparisons. Using our model, we conduct a systematic study of the parameter space and its relationship to different aspects of pearlescent appearance. We observe that several previously ignored parameters have a substantial impact on the material's optical behavior, including the multi-layered nature of modern interference pigments, correlations in the orientation of pigment particles, and variability in their properties (e.g. thickness). The utility of a general model for pearlescence extends far beyond computer graphics: inverse and differentiable approaches to rendering are increasingly used to disentangle the physics of scattering from real-world observations. Our approach could inform such reconstructions to enable the predictive design of tailored pearlescent materials.

Virtual Reality (VR) systems increase immersion by reproducing users' movements in the real world. However, several works have shown that this real-to-virtual mapping does not need to be precise in order to convey a realistic experience. Being able to alter this mapping has many potential applications, since achieving an accurate real-to-virtual mapping is not always possible due to limitations in the capture or display hardware, or in the physical space available. In this work, we measure detection thresholds for lateral translation gains of virtual camera motion in response to the corresponding head motion under natural viewing, and in the absence of locomotion, so that virtual camera movement can be either compressed or expanded while these manipulations remain undetected. Finally, we propose three applications for our method, addressing three key problems in VR: improving 6-DoF viewing for captured 360° footage, overcoming physical constraints, and reducing simulator sickness. We have further validated our thresholds and evaluated our applications by means of additional user studies confirming that our manipulations remain imperceptible, and showing that (i) compressing virtual camera motion reduces visible artifacts in 6-DoF, hence improving perceived quality, (ii) virtual expansion allows for completion of virtual tasks within a reduced physical space, and (iii) simulator sickness may be alleviated in simple scenarios when our compression method is applied.

We analyze the effect of motion in the perception of material appearance. First, we create a set of stimuli containing 72 realistic materials, rendered with varying degrees of linear motion blur. Then we launch a large-scale study on Mechanical Turk to rate a given set of perceptual attributes, such as brightness, roughness, or the perceived strength of reflections. Our statistical analysis shows that certain attributes undergo a significant change, varying appearance perception under motion. In addition, we further investigate the perception of brightness, for the particular cases of rubber and plastic materials. We create new stimuli, with ten different luminance levels and seven motion degrees. We launch a new user study to retrieve their perceived brightness. From the users’ judgements, we build two-dimensional maps showing how perceived brightness varies as a function of the luminance and motion of the material.

Abstract Several image editing methods have been proposed in the past decades, achieving brilliant results. The most sophisticated of them, however, require additional information per‐pixel. For instance, dehazing requires a specific transmittance value per pixel, or depth of field blurring requires depth or disparity values per pixel. This additional per‐pixel value is obtained either through elaborated heuristics or through additional control over the capture hardware, which is very often tailored for the specific editing application. In contrast, however, we propose a generic editing paradigm that can become the base of several different applications. This paradigm generates both the needed per‐pixel values and the resulting edit at interactive rates, with minimal user input that can be iteratively refined. Our key insight for getting per‐pixel values at such speed is to cluster them into superpixels, but, instead of a constant value per superpixel (which yields accuracy problems), we have a mathematical expression for pixel values at each superpixel: in our case, an order two multinomial per superpixel. This leads to a linear least‐squares system, effectively enabling specific per‐pixel values at fast speeds. We illustrate this approach in three applications: depth of field blurring (from depth values), dehazing (from transmittance values) and tone mapping (from brightness and contrast local values), and our approach proves both favorably interactive and accurate in all three. Our technique is also evaluated with a common dataset and compared favorably.

Abstract In this work, we introduce a novel algorithm for transient rendering in participating media. Our method is consistent, robust and is able to generate animations of time‐resolved light transport featuring complex caustic light paths in media. We base our method on the observation that the spatial continuity provides an increased coverage of the temporal domain, and generalize photon beams to transient‐state. We extend stead‐state photon beam radiance estimates to include the temporal domain. Then, we develop a progressive variant of our approach which provably converges to the correct solution using finite memory by averaging independent realizations of the estimates with progressively reduced kernel bandwidths. We derive the optimal convergence rates accounting for space and time kernels, and demonstrate our method against previous consistent transient rendering methods for participating media.

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.

We present a method for adding parallax and real-time playback of 360° videos in Virtual Reality headsets. In current video players, the playback does not respond to translational head movement, which reduces the feeling of immersion, and causes motion sickness for some viewers. Given a 360° video and its corresponding depth (provided by current stereo 360° stitching algorithms), a naive image-based rendering approach would use the depth to generate a 3D mesh around the viewer, then translate it appropriately as the viewer moves their head. However, this approach breaks at depth discontinuities, showing visible distortions, whereas cutting the mesh at such discontinuities leads to ragged silhouettes and holes at disocclusions. We address these issues by improving the given initial depth map to yield cleaner, more natural silhouettes. We rely on a three-layer scene representation, made up of a foreground layer and two static background layers, to handle disocclusions by propagating information from multiple frames for the first background layer, and then inpainting for the second one. Our system works with input from many of today's most popular 360° stereo capture devices (e.g., Yi Halo or GoPro Odyssey), and works well even if the original video does not provide depth information. Our user studies confirm that our method provides a more compelling viewing experience than without parallax, increasing immersion while reducing discomfort and nausea.

Abstract Power saving is a prevailing concern in desktop computers and, especially, in battery‐powered devices such as mobile phones. This is generating a growing demand for power‐aware graphics applications that can extend battery life, while preserving good quality. In this paper, we address this issue by presenting a real‐time power‐efficient rendering framework, able to dynamically select the rendering configuration with the best quality within a given power budget. Different from the current state of the art, our method does not require precomputation of the whole camera‐view space, nor Pareto curves to explore the vast power‐error space; as such, it can also handle dynamic scenes. Our algorithm is based on two key components: our novel power prediction model, and our runtime quality error estimation mechanism. These components allow us to search for the optimal rendering configuration at runtime, being transparent to the user. We demonstrate the performance of our framework on two different platforms: a desktop computer, and a mobile device. In both cases, we produce results close to the maximum quality, while achieving significant power savings.

We introduce a non-exponential radiative framework that takes into account the local spatial correlation of scattering particles in a medium. Most previous works in graphics have ignored this, assuming uncorrelated media with a uniform, random local distribution of particles. However, positive and negative correlation lead to slower- and faster-than-exponential attenuation respectively, which cannot be predicted by the Beer-Lambert law. As our results show, this has a major effect on extinction, and thus appearance. From recent advances in neutron transport, we first introduce our Extended Generalized Boltzmann Equation, and develop a general framework for light transport in correlated media. We lift the limitations of the original formulation, including an analysis of the boundary conditions, and present a model suitable for computer graphics, based on optical properties of the media and statistical distributions of scatterers. In addition, we present an analytic expression for transmittance in the case of positive correlation, and show how to incorporate it efficiently into a Monte Carlo renderer. We show results with a wide range of both positive and negative correlation, and demonstrate the differences compared to classic light transport.

Microfacet theory concisely models light transport over rough surfaces. Specular reflection is the result of single mirror reflections on each facet, while exact computation of multiple scattering is either neglected, or modeled using costly importance sampling techniques. Practical but accurate simulation of multiple scattering in microfacet theory thus remains an open challenge. In this work, we revisit the traditional V-groove cavity model and derive an analytical, cost-effective solution for multiple scattering in rough surfaces. Our kaleidoscopic model is made up of both real and virtual V-grooves, and allows us to calculate higher-order scattering in the microfacets in an analytical fashion. We then extend our model to include nonsymmetric grooves, allowing for additional degrees of freedom on the surface geometry, improving multiple reflections at grazing angles with backward compatibility to traditional normal distribution functions. We validate the accuracy of our model against ground-truth Monte Carlo simulations, and demonstrate its flexibility on anisotropic and textured materials. Our model is analytical, does not introduce significant cost and variance, can be seamless integrated in any rendering engine, preserves reciprocity and energy conservation, and is suitable for bidirectional methods.

We present a second-order gradient analysis of light transport in participating media and use this to develop an improved radiance caching algorithm for volumetric light transport. We adaptively sample and interpolate radiance from sparse points in the medium using a second-order Hessian-based error metric to determine when interpolation is appropriate. We derive our metric from each point’s incoming light field, computed by using a proxy triangulation-based representation of the radiance reflected by the surrounding medium and geometry. We use this representation to efficiently compute the first- and second-order derivatives of the radiance at the cache points while accounting for occlusion changes. We also propose a self-contained 2D model for light transport in media and use it to validate and analyze our approach, demonstrating that our method outperforms previous radiance caching algorithms both in terms of accurate derivative estimates and final radiance extrapolation. We generalize these findings to practical 3D scenarios, where we show improved results while reducing computation time by up to 30% compared to previous work.

Capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) of 3D objects with just a single, hand-held camera (such as an off-the-shelf smartphone or a DSLR camera) is a difficult, open problem. Previous works are either limited to planar geometry, or rely on previously scanned 3D geometry, thus limiting their practicality. There are several technical challenges that need to be overcome: First, the built-in flash of a camera is almost colocated with the lens, and at a fixed position; this severely hampers sampling procedures in the light-view space. Moreover, the near-field flash lights the object partially and unevenly. In terms of geometry, existing multiview stereo techniques assume diffuse reflectance only, which leads to overly smoothed 3D reconstructions, as we show in this paper. We present a simple yet powerful framework that removes the need for expensive, dedicated hardware, enabling practical acquisition of SVBRDF information from real-world, 3D objects with a single, off-the-shelf camera with a built-in flash. In addition, by removing the diffuse reflection assumption and leveraging instead such SVBRDF information, our method outputs high-quality 3D geometry reconstructions, including more accurate high-frequency details than state-of-the-art multiview stereo techniques. We formulate the joint reconstruction of SVBRDFs, shading normals, and 3D geometry as a multi-stage, iterative inverse-rendering reconstruction pipeline. Our method is also directly applicable to any existing multiview 3D reconstruction technique. We present results of captured objects with complex geometry and reflectance; we also validate our method numerically against other existing approaches that rely on dedicated hardware, additional sources of information, or both.

Understanding how people explore immersive virtual environments is crucial for many applications, such as designing virtual reality (VR) content, developing new compression algorithms, or learning computational models of saliency or visual attention. Whereas a body of recent work has focused on modeling saliency in desktop viewing conditions, VR is very different from these conditions in that viewing behavior is governed by stereoscopic vision and by the complex interaction of head orientation, gaze, and other kinematic constraints. To further our understanding of viewing behavior and saliency in VR, we capture and analyze gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions. We provide a thorough analysis of our data, which leads to several important insights, such as the existence of a particular fixation bias, which we then use to adapt existing saliency predictors to immersive VR conditions. In addition, we explore other applications of our data and analysis, including automatic alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and saliency-basedcompression.

Abstract Accurately modeling how light interacts with cloth is challenging, due to the volumetric nature of cloth appearance and its multiscale structure, where microstructures play a major role in the overall appearance at higher scales. Recently, significant effort has been put on developing better microscopic models for cloth structure, which have allowed rendering fabrics with unprecedented fidelity. However, these highly‐detailed representations still make severe simplifications on the scattering by individual fibers forming the cloth, ignoring the impact of fibers' shape, and avoiding to establish connections between the fibers' appearance and their optical and fabrication parameters. In this work we put our focus in the scattering of individual cloth fibers; we introduce a physically‐based scattering model for fibers based on their low‐level optical and geometric properties, relying on the extensive textile literature for accurate data. We demonstrate that scattering from cloth fibers exhibits much more complexity than current fiber models, showing important differences between cloth type, even in averaged conditions due to longer views. Our model can be plugged in any framework for cloth rendering, matches scattering measurements from real yarns, and is based on actual parameters used in the textile industry, allowing predictive bottom‐up definition of cloth appearance.

Abstract We present a method to automatically decompose a light field into its intrinsic shading and albedo components. Contrary to previous work targeted to two‐dimensional (2D) single images and videos, a light field is a 4D structure that captures non‐integrated incoming radiance over a discrete angular domain. This higher dimensionality of the problem renders previous state‐of‐the‐art algorithms impractical either due to their cost of processing a single 2D slice, or their inability to enforce proper coherence in additional dimensions. We propose a new decomposition algorithm that jointly optimizes the whole light field data for proper angular coherence. For efficiency, we extend Retinex theory, working on the gradient domain, where new albedo and occlusion terms are introduced. Results show that our method provides 4D intrinsic decompositions difficult to achieve with previous state‐of‐the‐art algorithms. We further provide a comprehensive analysis and comparisons with existing intrinsic image/video decomposition methods on light field images.

Abstract Video capture is limited by the trade‐off between spatial and temporal resolution: when capturing videos of high temporal resolution, the spatial resolution decreases due to bandwidth limitations in the capture system. Achieving both high spatial and temporal resolution is only possible with highly specialized and very expensive hardware, and even then the same basic trade‐off remains. The recent introduction of compressive sensing and sparse reconstruction techniques allows for the capture of single‐shot high‐speed video, by coding the temporal information in a single frame, and then reconstructing the full video sequence from this single‐coded image and a trained dictionary of image patches. In this paper, we first analyse this approach, and find insights that help improve the quality of the reconstructed videos. We then introduce a novel technique, based on convolutional sparse coding (CSC), and show how it outperforms the state‐of‐the‐art, patch‐based approach in terms of flexibility and efficiency, due to the convolutional nature of its filter banks. The key idea for CSC high‐speed video acquisition is extending the basic formulation by imposing an additional constraint in the temporal dimension, which enforces sparsity of the first‐order derivatives over time.

Abstract Reproducing the appearance of real‐world materials using current printing technology is problematic. The reduced number of inks available define the printer's limited gamut, creating distortions in the printed appearance that are hard to control. Gamut mapping refers to the process of bringing an out‐of‐gamut material appearance into the printer's gamut, while minimizing such distortions as much as possible. We present a novel two‐step gamut mapping algorithm that allows users to specify which perceptual attribute of the original material they want to preserve (such as brightness, or roughness). In the first step, we work in the low‐dimensional intuitive appearance space recently proposed by Serrano et al. [ SGM*16 ], and adjust achromatic reflectance via an objective function that strives to preserve certain attributes. From such intermediate representation, we then perform an image‐based optimization including color information, to bring the BRDF into gamut. We show, both objectively and through a user study, how our method yields superior results compared to the state of the art, with the additional advantage that the user can specify which visual attributes need to be preserved. Moreover, we show how this approach can also be used for attribute‐preserving material editing.

We present a novel, compact single-shot hyperspectral imaging method. It enables capturing hyperspectral images using a conventional DSLR camera equipped with just an ordinary refractive prism in front of the camera lens. Our computational imaging method reconstructs the full spectral information of a scene from dispersion over edges. Our setup requires no coded aperture mask, no slit, and no collimating optics, which are necessary for traditional hyperspectral imaging systems. It is thus very cost-effective, while still highly accurate. We tackle two main problems: First, since we do not rely on collimation, the sensor records a projection of the dispersion information, distorted by perspective. Second, available spectral cues are sparse, present only around object edges. We formulate an image formation model that can predict the perspective projection of dispersion, and a reconstruction method that can estimate the full spectral information of a scene from sparse dispersion information. Our results show that our method compares well with other state-of-the-art hyperspectral imaging systems, both in terms of spectral accuracy and spatial resolution, while being orders of magnitude cheaper than commercial imaging systems.

We present a novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches. Our method consists of two steps: First, we learn nonlinear spectral representations from real-world hyperspectral datasets; for this, we build a convolutional autoencoder which allows reconstructing its own input through its encoder and decoder networks. Second, we introduce a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain, by means of our new fidelity prior. Our technique can be applied to any existing compressive imaging architecture, and has been thoroughly tested both in simulation, and by building a prototype hyperspectral imaging system. It outperforms the state-of-the-art methods from each architecture, both in terms of spectral accuracy and spatial resolution, while its computational complexity is reduced by two orders of magnitude with respect to sparse coding techniques. Moreover, we present two additional applications of our method: hyperspectral interpolation and demosaicing. Last, we have created a new high-resolution hyperspectral dataset containing sharper images of more spectral variety than existing ones, available through our project website.

Time-of-flight (ToF) imaging has become a widespread technique for depth estimation, allowing affordable off-the-shelf cameras to provide depth maps in real time. However, multipath interference (MPI) resulting from indirect illumination significantly degrades the captured depth. Most previous works have tried to solve this problem by means of complex hardware modifications or costly computations. In this work, we avoid these approaches and propose a new technique to correct errors in depth caused by MPI, which requires no camera modifications and takes just 10 milliseconds per frame. Our observations about the nature of MPI suggest that most of its information is available in image space; this allows us to formulate the depth imaging process as a spatially-varying convolution and use a convolutional neural network to correct MPI errors. Since the input and output data present similar structure, we base our network on an autoencoder, which we train in two stages. First, we use the encoder (convolution filters) to learn a suitable basis to represent MPI-corrupted depth images; then, we train the decoder (deconvolution filters) to correct depth from synthetic scenes, generated by using a physically-based, time-resolved renderer. This approach allows us to tackle a key problem in ToF, the lack of ground-truth data, by using a large-scale captured training set with MPI-corrupted depth to train the encoder, and a smaller synthetic training set with ground truth depth to train the decoder stage of the network. We demonstrate and validate our method on both synthetic and real complex scenarios, using an off-the-shelf ToF camera, and with only the captured, incorrect depth as input.

Traditional cinematography has relied for over a century on a well-established set of editing rules, called continuity editing, to create a sense of situational continuity. Despite massive changes in visual content across cuts, viewers in general experience no trouble perceiving the discontinuous flow of information as a coherent set of events. However, Virtual Reality (VR) movies are intrinsically different from traditional movies in that the viewer controls the camera orientation at all times. As a consequence, common editing techniques that rely on camera orientations, zooms, etc., cannot be used. In this paper we investigate key relevant questions to understand how well traditional movie editing carries over to VR, such as: Does the perception of continuity hold across edit boundaries? Under which conditions? Does viewers' observational behavior change after the cuts? To do so, we rely on recent cognition studies and the event segmentation theory, which states that our brains segment continuous actions into a series of discrete, meaningful events. We first replicate one of these studies to assess whether the predictions of such theory can be applied to VR. We next gather gaze data from viewers watching VR videos containing different edits with varying parameters, and provide the first systematic analysis of viewers' behavior and the perception of continuity in VR. From this analysis we make a series of relevant findings; for instance, our data suggests that predictions from the cognitive event segmentation theory are useful guides for VR editing; that different types of edits are equally well understood in terms of continuity; and that spatial misalignments between regions of interest at the edit boundaries favor a more exploratory behavior even after viewers have fixated on a new region of interest. In addition, we propose a number of metrics to describe viewers' attentional behavior in VR. We believe the insights derived from our work can be useful as guidelines for VR content creation.

Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference.

Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation.

Abstract Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially‐varying pixel exposures. In this paper, we propose a novel algorithm to recover high‐quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently‐introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher‐quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.

With recent advances on mobile computing, power consumption has become a significant limiting constraint for many graphics applications. As a result, rendering on a power budget arises as an emerging demand. In this paper, we present a real-time, power-optimal rendering framework to address this problem, by finding the optimal rendering settings that minimize power consumption while maximizing visual quality. We first introduce a novel power-error, multi-objective cost space, and formally formulate power saving as an optimization problem. Then, we develop a two-step algorithm to efficiently explore the vast power-error space and leverage optimal Pareto frontiers at runtime. Finally, we show that our rendering framework can be generalized across different platforms, desktop PC or mobile device, by demonstrating its performance on our own OpenGL rendering framework, as well as the commercially available Unreal Engine.

Acquiring microscale reflectance and normals is useful for digital documentation and identification of real-world materials. However, its simultaneous acquisition has rarely been explored due to the difficulties of combining both sources of information at such small scale. In this paper, we capture both spatially-varying material appearance (diffuse, specular and roughness) and normals simultaneously at the microscale resolution. We design and build a microscopic light dome with 374 LED lights over the hemisphere, specifically tailored to the characteristics of microscopic imaging. This allows us to achieve the highest resolution for such combined information among current state-of-the-art acquisition systems. We thoroughly test and characterize our system, and provide microscopic appearance measurements of a wide range of common materials, as well as renderings of novel views to validate the applicability of our captured data. Additional applications such as bi-scale material editing from real-world samples are also demonstrated.

We propose a novel birefractive depth acquisition method, which allows for single-shot depth imaging by just placing a birefringent material in front of the lens. While most transmissive materials present a single refractive index per wavelength, birefringent crystals like calcite posses two, resulting in a double refraction effect. We develop an imaging model that leverages this phenomenon and the information contained in the ordinary and the extraordinary refracted rays, providing an effective formulation of the geometric relationship between scene depth and double refraction. To handle the inherent ambiguity of having two sources of information overlapped in a single image, we define and combine two different cost volume functions. We additionally present a novel calibration technique for birefringence, carefully analyze and validate our model, and demonstrate the usefulness of our approach with several image-editing applications.

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction.

Physical simulation and rendering of cloth is widely used in 3D graphics applications to create realistic and compelling scenes. However, cloth animation can be slow to compute and difficult to specify. In this paper, we present a set of experiments in which we explore some factors that contribute to the perception of cloth, to determine how efficiency could be improved without sacrificing realism. Using real video footage of several fabrics covering a wide range of visual appearances and dynamic behaviors, and their simulated counterparts, we explore the interplay of visual appearance and dynamics in cloth animation.

Abstract This paper presents a time‐varying, multi‐layered biophysically‐based model of the optical properties of human skin, suitable for simulating appearance changes due to aging. We have identified the key aspects that cause such changes, both in terms of the structure of skin and its chromophore concentrations, and rely on the extensive medical and optical tissue literature for accurate data. Our model can be expressed in terms of biophysical parameters, optical parameters commonly used in graphics and rendering (such as spectral absorption and scattering coefficients), or more intuitively with higher‐level parameters such as age, gender, skin care or skin type. It can be used with any rendering algorithm that uses diffusion profiles, and it allows to automatically simulate different types of skin at different stages of aging, avoiding the need for artistic input or costly capture processes. While the presented skin model is inspired on tissue optics studies, we also provided a simplified version valid for non‐diagnostic applications.

Abstract We present a real‐time framework which allows interactive visualization of relativistic effects for time‐resolved light transport. We leverage data from two different sources: real‐world data acquired with an effective exposure time of less than 2 picoseconds, using an ultra‐fast imaging technique termed femto‐photography, and a transient renderer based on ray‐tracing. We explore the effects of time dilation, light aberration, frequency shift and radiance accumulation by modifying existing models of these relativistic effects to take into account the time‐resolved nature of light propagation. Unlike previous works, we do not impose limiting constraints in the visualization, allowing the virtual camera to explore freely a reconstructed 3D scene depicting dynamic illumination. Moreover, we consider not only linear motion, but also acceleration and rotation of the camera. We further introduce, for the first time, a pinhole camera model into our relativistic rendering framework, and account for subsequent changes in focal length and field of view as the camera moves through the scene.

In this paper, we propose two real‐time models for simulating subsurface scattering for a large variety of translucent materials, which need under 0.5 ms per frame to execute. This makes them a practical option for real‐time production scenarios. Current state‐of‐the‐art, real‐time approaches simulate subsurface light transport by approximating the radially symmetric non‐separable diffusion kernel with a sum of separable Gaussians, which requires multiple (up to 12) 1D convolutions. In this work we relax the requirement of radial symmetry to approximate a 2D diffuse reflectance profile by a single separable kernel. We first show that low‐rank approximations based on matrix factorization outperform previous approaches, but they still need several passes to get good results. To solve this, we present two different separable models: the first one yields a high‐quality diffusion simulation, while the second one offers an attractive trade‐off between physical accuracy and artistic control. Both allow rendering of subsurface scattering using only two 1D convolutions, reducing both execution time and memory consumption, while delivering results comparable to techniques with higher cost. Using our importance‐sampling and jittering strategies, only seven samples per pixel are required. Our methods can be implemented as simple post‐processing steps without intrusive changes to existing rendering pipelines.

No abstract available.

Virtual characters contribute strongly to the entire visuals of 3D animated films. However, designing believable characters remains a challenging task. Artists rely on stylization to increase appeal or expressivity, exaggerating or softening specific features. In this paper we analyze two of the most influential factors that define how a character looks: shape and material. With the help of artists, we design a set of carefully crafted stimuli consisting of different stylization levels for both parameters, and analyze how different combinations affect the perceived realism, appeal, eeriness, and familiarity of the characters. Moreover, we additionally investigate how this affects the perceived intensity of different facial expressions (sadness, anger, happiness, and surprise). Our experiments reveal that shape is the dominant factor when rating realism and expression intensity, while material is the key component for appeal. Furthermore our results show that realism alone is a bad predictor for appeal, eeriness, or attractiveness.

We present a thorough study to evaluate different light field editing interfaces, tools and workflows from a user perspective. This is of special relevance given the multidimensional nature of light fields, which may make common image editing tasks become complex in light field space. We additionally investigate the potential benefits of using depth information when editing, and the limitations imposed by imperfect depth reconstruction using current techniques. We perform two different experiments, collecting both objective and subjective data from a varied number of editing tasks of increasing complexity based on local point-and-click tools. In the first experiment, we rely on perfect depth from synthetic light fields, and focus on simple edits. This allows us to gain basic insight on light field editing, and to design a more advanced editing interface. This is then used in the second experiment, employing real light fields with imperfect reconstructed depth, and covering more advanced editing tasks. Our study shows that users can edit light fields with our tested interface and tools, even in the presence of imperfect depth. They follow different workflows depending on the task at hand, mostly relying on a combination of different depth cues. Last, we confirm our findings by asking a set of artists to freely edit both real and synthetic light fields.

Recently, we have seen a growing trend in the design and fabrication of personalized figurines, created by scanning real people and then physically reproducing miniature statues with 3D printers. This is currently a hot topic both in academia and industry, and the printed figurines are gaining more and more realism, especially with state-of-the-art facial scanning technology improving. However, current systems all contain the same limitation - no previous method is able to suitably capture personalized hair-styles for physical reproduction. Typically, the subject's hair is approximated very coarsely or replaced completely with a template model. In this paper we present the first method for stylized hair capture, a technique to reconstruct an individual's actual hair-style in a manner suitable for physical reproduction. Inspired by centuries-old artistic sculptures, our method generates hair as a closed-manifold surface, yet contains the structural and color elements stylized in a way that captures the defining characteristics of the hair-style. The key to our approach is a novel multi-view stylization algorithm, which extends feature-preserving color filtering from 2D images to irregular manifolds in 3D, and introduces abstract geometric details that are coherent with the color stylization. The proposed technique fits naturally in traditional pipelines for figurine reproduction, and we demonstrate the robustness and versatility of our approach by capturing several subjects with widely varying hair-styles.

This paper presents a method for measuring the similarity in style between two pieces of vector art, independent of content. Similarity is measured by the differences between four types of features: color, shading, texture, and stroke. Feature weightings are learned from crowdsourced experiments. This perceptual similarity enables style-based search. Using our style-based search feature, we demonstrate an application that allows users to create stylistically-coherent clip art mash-ups.

Recent advances in ultra-fast imaging have triggered many promising applications in graphics and vision, such as capturing transparent objects, estimating hidden geometry and materials, or visualizing light in motion. There is, however, very little work regarding the effective simulation and analysis of transient light transport, where the speed of light can no longer be considered infinite. We first introduce the transient path integral framework, formally describing light transport in transient state. We then analyze the difficulties arising when considering the light's time-of-flight in the simulation (rendering) of images and videos. We propose a novel density estimation technique that allows reusing sampled paths to reconstruct time-resolved radiance, and devise new sampling strategies that take into account the distribution of radiance along time in participating media. We then efficiently simulate time-resolved phenomena (such as caustic propagation, fluorescence or temporal chromatic dispersion), which can help design future ultra-fast imaging devices using an analysis-by-synthesis approach, as well as to achieve a better understanding of the nature of light transport.

We present a method to decompose a video into its intrinsic components of reflectance and shading, plus a number of related example applications in video editing such as segmentation, stylization, material editing, recolorization and color transfer. Intrinsic decomposition is an ill-posed problem, which becomes even more challenging in the case of video due to the need for temporal coherence and the potentially large memory requirements of a global approach. Additionally, user interaction should be kept to a minimum in order to ensure efficiency. We propose a probabilistic approach, formulating a Bayesian Maximum a Posteriori problem to drive the propagation of clustered reflectance values from the first frame, and defining additional constraints as priors on the reflectance and shading. We explicitly leverage temporal information in the video by building a causal-anticausal, coarse-to-fine iterative scheme, and by relying on optical flow information. We impose no restrictions on the input video, and show examples representing a varied range of difficult cases. Our method is the first one designed explicitly for video; moreover, it naturally ensures temporal consistency, and compares favorably against the state of the art in this regard.

The BTF data structure was a breakthrough for appearance modeling in computer graphics. More research is needed though to make BTFs practical in rendering applications. We present the first systematic study of the effects of Approximate filtering on the appearance of BTFs, by exploring the spatial, angular and temporal domains over a varied set of stimuli. We perform our initial experiments on simple geometry and lighting, and verify our observations on more complex settings. We consider multi-dimensional filtering versus conventional mipmapping, and find that multi-dimensional filtering produces superior results. We examine the tradeoff between under- and oversampling, and find that different filtering strategies can be applied in each domain, while maintaining visual equivalence with respect to a ground truth. For example, we find that preserving contrast is more important in static than dynamic images, indicating greater levels of spatial filtering are possible for animations. We find that filtering can be performed more aggressively in the angular domain than in the spatial. Additionally, we find that high-level visual descriptors of the BTF are linked to the perceptual performance of pre-filtered approximations. In turn, some of these high-level descriptors correlate with low level statistics of the BTF. We show six different practical applications of applying our findings to improving filtering, rendering and compression strategies.

Abstract We present a novel computational framework for physically and chemically‐based simulations of analog alternative photographic processes. In the real world, these processes allow the creation of very personal and unique depictions due to the combination of the chemicals used, the physical interaction with liquid solutions, and the individual craftsmanship of the artist. Our work focuses not only on achieving similar compelling results, but on the manual process as well, introducing a novel exploratory approach for interactive digital image creation and manipulation. With such an emphasis on the user interaction, our simulations are devised to run on tablet devices; thus we propose the combination of a lightweight data‐driven model to simulate the chemical reactions involved, with efficient fluids simulations that modulate them. This combination allows realistic gestures‐based user interaction with constant visual feedback in real‐time. Using the proposed framework, we have built two prototypes with different tradeoffs between realism and flexibility, showing its potential to build novel image editing tools.

Abstract Many high‐level image processing tasks require an estimate of the positions, directions and relative intensities of the light sources that illuminated the depicted scene. In image‐based rendering, augmented reality and computer vision, such tasks include matching image contents based on illumination, inserting rendered synthetic objects into a natural image, intrinsic images, shape from shading and image relighting. Yet, accurate and robust illumination estimation, particularly from a single image, is a highly ill‐posed problem. In this paper, we present a new method to estimate the illumination in a single image as a combination of achromatic lights with their 3D directions and relative intensities. In contrast to previous methods, we base our azimuth angle estimation on curve fitting and recursive refinement of the number of light sources. Similarly, we present a novel surface normal approximation using an osculating arc for the estimation of zenith angles. By means of a new data set of ground‐truth data and images, we demonstrate that our approach produces more robust and accurate results, and show its versatility through novel applications such as image compositing and analysis.

We propose a novel metric of visual comfort for stereoscopic motion, based on a series of systematic perceptual experiments. We take into account disparity, motion in depth, motion on the screen plane, and the spatial frequency of luminance contrast. We further derive a comfort metric to predict the comfort of short stereoscopic videos. We validate it on both controlled scenes and real videos available on the internet, and show how all the factors we take into account, as well as their interactions, affect viewing comfort. Last, we propose various applications that can benefit from our comfort measurements and metric.

We present femto-photography , a novel imaging technique to capture and visualize the propagation of light. With an effective exposure time of 1.85 picoseconds (ps) per frame, we reconstruct movies of ultrafast events at an equivalent resolution of about one half trillion frames per second. Because cameras with this shutter speed do not exist, we re-purpose modern imaging hardware to record an ensemble average of repeatable events that are synchronized to a streak sensor, in which the time of arrival of light from the scene is coded in one of the sensor's spatial dimensions. We introduce reconstruction methods that allow us to visualize the propagation of femtosecond light pulses through macroscopic scenes; at such fast resolution, we must consider the notion of time-unwarping between the camera's and the world's space-time coordinate systems to take into account effects associated with the finite speed of light. We apply our femto-photography technique to visualizations of very different scenes, which allow us to observe the rich dynamics of time-resolved light transport effects, including scattering, specular reflections, diffuse interreflections, diffraction, caustics, and subsurface scattering. Our work has potential applications in artistic, educational, and scientific visualizations; industrial imaging to analyze material properties; and medical imaging to reconstruct subsurface elements. In addition, our time-resolved technique may motivate new forms of computational photography.

One of the most challenging tasks for an animator is to quickly create convincing facial expressions. Finding an effective control interface to manipulate facial geometry has traditionally required experienced users (usually technical directors), who create and place the necessary animation controls. Here we present our sketching interface control system, designed to reduce the time and effort necessary to create facial animations. Inspired in the way artists draw, where simple strokes define the shape of an object, our approach allows the user to sketch such strokes either directly on the 3D mesh or on two different types of canvas: a 2D fixed canvas or more flexible 2.5D dynamic screen-aligned billboards. In all cases, the strokes do not control the geometry of the face, but the underlying animation rig instead, allowing direct manipulation of the rig elements. Additionally, we show how the strokes can be easily reused in different characters, allowing retargeting of poses on several models. We illustrate our interactive approach using varied facial models of different styles showing that first time users typically create appealing 3D poses and animations in just a few minutes. We also present in this article the results of a user study. We deploy our method in an application for an artistic purpose. Our system has also been used in a pioneer serious game context, where the goal was to teach people with Autism Spectrum Disorders (ASD) to recognize facial emotions, using real time synthesis and automatic facial expression analysis.

Abstract Decomposing an input image into its intrinsic shading and reflectance components is a long‐standing ill‐posed problem. We present a novel algorithm that requires no user strokes and works on a single image. Based on simple assumptions about its reflectance and luminance, we first find clusters of similar reflectance in the image, and build a linear system describing the connections and relations between them. Our assumptions are less restrictive than widely‐adopted Retinex‐based approaches, and can be further relaxed in conflicting situations. The resulting system is robust even in the presence of areas where our assumptions do not hold. We show a wide variety of results, including natural images, objects from the MIT dataset and texture images, along with several applications, proving the versatility of our method.

Abstract Rendering realistic illumination effects for complex animated scenes with many dynamic objects or characters is computationally expensive. Yet, it is not obvious how important such accurate lighting is for the overall perceived realism in these scenes. In this paper, we present a methodology to evaluate the perceived fidelity of illumination in scenes with dynamic aggregates, such as crowds, and explore several factors which may affect this perception. We focus in particular on evaluating how a popular spherical harmonics lighting method can be used to approximate realistic lighting of crowds. We conduct a series of psychophysical experiments to explore how a simple approach to approximating global illumination, using interpolation in the temporal domain, affects the perceived fidelity of dynamic scenes with high geometric, motion, and illumination complexity. We show that the complexity of the geometry and temporal properties of the crowd entities, the motion of the aggregate as a whole, the type of interpolation (i.e., of the direct and/or indirect illumination coefficients), and the presence or absence of colour all affect perceived fidelity. We show that high (i.e., above 75%) levels of perceived scene fidelity can be maintained while interpolating indirect illumination for intervals of up to 30 frames, resulting in a greater than three‐fold rendering speed‐up.

Abstract We present a new image‐based, post‐processing antialiasing technique, which offers practical solutions to the common, open problems of existing filter‐based real‐time antialiasing algorithms. Some of the new features include local contrast analysis for more reliable edge detection, and a simple and effective way to handle sharp geometric features and diagonal lines. This, along with our accelerated and accurate pattern classification allows for a better reconstruction of silhouettes. Our method shows for the first time how to combine morphological antialiasing (MLAA) with additional multi/supersampling strategies (MSAA, SSAA) for accurate subpixel features, and how to couple it with temporal reprojection; always preserving the sharpness of the image. All these solutions combine synergies making for a very robust technique, yielding results of better overall quality than previous approaches while more closely converging to MSAA/SSAA references but maintaining extremely fast execution times. Additionally, we propose different presets to better fit the available resources or particular needs of each scenario.

Abstract The field of computational photography, and in particular the design and implementation of coded apertures, has yielded impressive results in the last years. In this paper we introduce perceptually optimized coded apertures for defocused deblurring. We obtain near‐optimal apertures by means of optimization, with a novel evaluation function that includes two existing image quality perceptual metrics. These metrics favour results where errors in the final deblurred images will not be perceived by a human observer. Our work improves the results obtained with a similar approach that only takes into account the L2 metric in the evaluation function.

In this article, we derive a physically-based model for simulating rainbows. Previous techniques for simulating rainbows have used either geometric optics (ray tracing) or Lorenz-Mie theory. Lorenz-Mie theory is by far the most accurate technique as it takes into account optical effects such as dispersion, polarization, interference, and diffraction. These effects are critical for simulating rainbows accurately. However, as Lorenz-Mie theory is restricted to scattering by spherical particles, it cannot be applied to real raindrops which are nonspherical, especially for larger raindrops. We present the first comprehensive technique for simulating the interaction of a wavefront of light with a physically-based water drop shape. Our technique is based on ray tracing extended to account for dispersion, polarization, interference, and diffraction. Our model matches Lorenz-Mie theory for spherical particles, but it also enables the accurate simulation of nonspherical particles. It can simulate many different rainbow phenomena including double rainbows and supernumerary bows. We show how the nonspherical raindrops influence the shape of the rainbows, and we provide a simulation of the rare twinned rainbow, which is believed to be caused by nonspherical water drops.

Retargeting refers to the process by which an image or video is adapted from the display device for which it was meant (target display) to another one (retarget display). The retarget display has different features from the target one such as dynamic range, discretization levels, color gamut, multi-view, and refresh rate spatial resolution. This is a very relevant topic in graphics, given the increasing number of display devices from large, high-contrast screens to small cell phones with limited dynamic range; a lot of techniques are being published in different venues, and it's hard to keep up. For most cases retargeting can be an ill-posed problem, for example in the process of displaying Low Dynamic Range (LDR) or 8-bit content on High Dynamic Range (HDR) displays. Such a problem requires the retargeting algorithm to generate new content which is missing in the input image/frame. In this course, we will present the latest solutions and techniques for retargeting images along various dimensions such as dynamic range, colors, temporal and spatial resolutions, and for the first time offer a much-needed holistic view of the field. Moreover, we are going to show how to measure and analyze the changes applied to an image or video in terms of quality using both psychophysical experiments (subjective) and computational metrics (objective). The course should be of interest to anyone involved in graphics in a broader sense, given the almost unavoidable need to retarget results to different devices -from developers interested in implementing retargeting techniques, to users that just need an overall perspective. For researchers fully engaged in developing multi-dimensional retargeting techniques, this course will serve as a solid background for future algorithms.

The advent of affordable display technology and seamless integration of real-world scenes with computer graphics fuels our continuing ability to create and display stunning realistic imagery. With the arrival of new technology, algorithms and display methods comes the realization that gains can be made by tailoring output to the intended audience - humans. Human beings have amazingly complex perceptual systems, which have the ability to quickly capture and process vast amounts of complex data. With all its capability however, the Human Visual System (HVS) has some surprising nuances and limitations that can be exploited to the benefit of numerous graphics applications. This short course will provide insight into those aspects of HVS and other perceptual systems that can serve as both a guide and yard-stick to further the development and evaluation of computer graphics imagery and presentations. The literature on perception provides a rich source of knowledge that can be applied to the realm of computer graphics for immediate and direct benefit, generating images that not only exhibit higher quality, but use less time and resources to process. In addition, knowledge of the HVS serves as a guide on how best to present the images to fulfill the application at hand.

Abstract This paper introduces a new method for simulating homogeneous subsurface light transport in translucent objects. Our approach is based on irradiance convolutions over a multi‐layered representation of the volume for light transport, which is general enough to obtain plausible depictions of translucent objects based on the diffusion approximation. We aim at providing an efficient physically based algorithm that can apply arbitrary diffusion profiles to general geometries. We obtain accurate results for a wide range of materials, on par with the hierarchical method by Jensen and Buhler.

Abstract We present a novel method to estimate an approximation of the reflectance characteristics of optically thick, homogeneous translucent materials using only a single photograph as input. First, we approximate the diffusion profile as a linear combination of piecewise constant functions, an approach that enables a linear system minimization and maximizes robustness in the presence of suboptimal input data inferred from the image. We then fit to a smoother monotonically decreasing model, ensuring continuity on its first derivative. We show the feasibility of our approach and validate it in controlled environments, comparing well against physical measurements from previous works. Next, we explore the performance of our method in uncontrolled scenarios, where neither lighting nor geometry are known. We show that these can be roughly approximated from the corresponding image by making two simple assumptions: that the object is lit by a distant light source and that it is globally convex, allowing us to capture the visual appearance of the photographed material. Compared with previous works, our technique offers an attractive balance between visual accuracy and ease of use, allowing its use in a wide range of scenarios including off‐the‐shelf, single images, thus extending the current repertoire of real‐world data acquisition techniques.

Abstract Motion blur is a fundamental cue in the perception of objects in motion. This phenomenon manifests as a visible trail along the trajectory of the object and is the result of the combination of relative motion and light integration taking place in film and electronic cameras. In this work, we analyse the mechanisms that produce motion blur in recording devices and the methods that can simulate it in computer generated images. Light integration over time is one of the most expensive processes to simulate in high‐quality renders, as such, we make an in‐depth review of the existing algorithms and we categorize them in the context of a formal model that highlights their differences, strengths and limitations. We finalize this report proposing a number of alternative classifications that will help the reader identify the best technique for a particular scenario.

Motion blur is a frequent requirement for the rendering of high-quality animated images. However, the computational resources involved are usually higher than those for images that have not been temporally antialiased. In this article we study the influence of high-level properties such as object material and speed, shutter time, and antialiasing level. Based on scenes containing variations of these parameters, we design different psychophysical experiments to determine how influential they are in the perception of image quality. This work gives insights on the effects these parameters have and exposes certain situations where motion blurred stimuli may be indistinguishable from a gold standard. As an immediate practical application, images of similar quality can be produced while the computing requirements are reduced. Algorithmic efforts have traditionally been focused on finding new improved methods to alleviate sampling artifacts by steering computation to the most important dimensions of the rendering equation. Concurrently, rendering algorithms can take advantage of certain perceptual limits to simplify and optimize computations. To our knowledge, none of them has identified nor used these limits in the rendering of motion blur. This work can be considered a first step in that direction.

Facial appearance depends on both the physical and physiological state of the skin. As people move, talk, undergo stress, and change expression, skin appearance is in constant flux. One of the key indicators of these changes is thecolorof skin. Skin color is determined by scattering and absorption of light within the skin layers, caused mostly by concentrations of two chromophores, melanin and hemoglobin. In this paper we present a real-time dynamic appearance model of skin built fromin vivomeasurements of melanin and hemoglobin concentrations. We demonstrate an efficient implementation of our method, and show that it adds negligible overhead to existing animation and rendering pipelines. Additionally, we develop a realistic, intuitive, and automatic control for skin color, which we term askin appearance rig.This rig can easily be coupled with a traditional geometric facial animation rig. We demonstrate our method by augmenting digital facial performance with realistic appearance changes.

The numerous works on media retargeting call for a methodological approach for evaluating retargeting results. We present the first comprehensive perceptual study and analysis of image retargeting. First, we create a benchmark of images and conduct a large scale user study to compare a representative number of state-of-the-art retargeting methods. Second, we present analysis of the users' responses, where we find that humans in general agree on the evaluation of the results and show that some retargeting methods are consistently more favorable than others. Third, we examine whether computational image distance metrics can predict human retargeting perception. We show that current measures used in this context are not necessarily consistent with human rankings, and demonstrate that better results can be achieved using image features that were not previously considered for this task. We also reveal specific qualities in retargeted media that are more important for viewers. The importance of our work lies in promoting better measures to assess and guide retargeting algorithms in the future. The full benchmark we collected, including all images, retargeted results, and the collected user data, are available to the research community for further investigation at http://people.csail.mit.edu/mrub/retargetme.

We propose a novel skin shader which translates the simulation of subsurface scattering from texture space to a screen-space diffusion approximation. It naturally scales well while maintaining a perceptually plausible result. This technique allows us to ensure real-time performance even when several characters may appear on screen at the same time. The visual realism of the resulting images is validated using a subjective psychophysical preference experiment. Our results show that, independent of distance and light position, the images rendered using our novel shader have as high visual realism as a previously developed physically-based shader.

Most existing image content has low dynamic range (LDR), which necessitates effective methods to display such legacy content on high dynamic range (HDR) devices. Reverse tone mapping operators (rTMOs) aim to take LDR content as input and adjust the contrast intelligently to yield output that recreates the HDR experience. In this paper we show that current rTMO approaches fall short when the input image is not exposed properly. More specifically, we report a series of perceptual experiments using a Brightside HDR display and show that, while existing rTMOs perform well for under-exposed input data, the perceived quality degrades substantially with over-exposure, to the extent that in some cases subjects prefer the LDR originals to images that have been treated with rTMOs. We show that, in these cases, a simple rTMO based on gamma expansion avoids the errors introduced by other methods, and propose a method to automatically set a suitable gamma value for each image, based on the image key and empirical data. We validate the results both by means of perceptual experiments and using a recent image quality metric, and show that this approach enhances visible details without causing artifacts in incorrectly-exposed regions. Additionally, we perform another set of experiments which suggest that spatial artifacts introduced by rTMOs are more disturbing than inaccuracies in the expanded intensities. Together, these findings suggest that when the quality of the input data is unknown, reverse tone mapping should be handled with simple, non-aggressive methods to achieve the desired effect.

Abstract Simulating the in‐water ocean light field is a daunting task. Ocean waters are one of the richest participating media, where light interacts not only with water molecules, but with suspended particles and organic matter as well. The concentration of each constituent greatly affects these interactions, resulting in very different hues. Inelastic scattering events such as fluorescence or Raman scattering imply energy transfers that are usually neglected in the simulations. Our contributions in this paper are a bio‐optical model of ocean waters suitable for computer graphics simulations, along with an improved method to obtain an accurate solution of the in‐water light field based on radiative transfer theory. The method provides a link between the inherent optical properties that define the medium and its apparent optical properties, which describe how it looks. The bio‐optical model of the ocean uses published data from oceanography studies. For inelastic scattering we compute all frequency changes at higher and lower energy values, based on the spectral quantum efficiency function of the medium. The results shown prove the usability of the system as a predictive rendering algorithm. Areas of application for this research span from underwater imagery to remote sensing; the resolution method is general enough to be usable in any type of participating medium simulation.

We present a powerful technique to simulate and approximate caustics in images. Our algorithm is designed to produce good results without the need to painstakingly paint over pixels. The ability to edit global illumination through image processing allows interaction with images at a level which has not yet been demonstrated, and significantly augments and extends current image-based material editing approaches. We show by means of a set of psychophysical experiments that the resulting imagery is visually plausible and on par with photon mapping, albeit without the need for hand-modeling the underlying geometry.

High-fidelity image synthesis is the process of computing images that are perceptually indistinguishable from the real world they are attempting to portray. Such a level of fidelity requires that the physical processes of materials and the behavior of light are accurately simulated. Most computer graphics algorithms assume that light passes freely between surfaces within an environment. However, in many applications, we also need to take into account how the light interacts with media, such as dust, smoke, fog, etc., between the surfaces. The computational requirements for calculating the interaction of light with such participating media are substantial. This process can take many hours and rendering effort is often spent on computing parts of the scene that may not be perceived by the viewer. In this paper, we present a novel perceptual strategy for physically based rendering of participating media. By using a combination of a saliency map with our new extinction map (X map), we can significantly reduce rendering times for inhomogeneous media. The visual quality of the resulting images is validated using two objective difference metrics and a subjective psychophysical experiment. Although the average pixel errors of these metric are all less than 1%, the subjective validation indicates that the degradation in quality still is noticeable for certain scenes. We thus introduce and validate a novel light map (L map) that accounts for salient features caused by multiple light scattering around light sources.

GIGA (the Advanced Computer Graphics Group from the University of Zaragoza) has developed two packages, code-named ALEPH and SICARA3D. Those systems are capable of both calculating light distribution in complex environments, based on a rigorous simulation of the physical phenomena involved, and visualizing the results in a photorealistic way, including models of the HVS (human visual system) implemented in a third package code-named S/spl middot/E/spl middot/K/spl middot/E/spl middot/R. Our systems support, as a remarkable difference with respect to all other known systems like RADIANCE ([G.J. Ward, (1994)]), a spectral management of lighting magnitudes, instead of working with simple RGB. We have also added to this framework the power of low-cost CAVE immersive systems. With this combination, we include stereoscopy and a collaborative environment to the reliability of the numerical results and the realistic aspect of the imagery.

In this paper, it is described a method of curved ray tracing capable of depicting phenomena that arise, under certain conditions, when light propagates through an inhomogeneous atmosphere. As an example application the distortions in the spherical shape of the sun during sunsets are modelled, including split suns, flattened suns and double suns. Nevertheless, the method is general and can be applied to any media in which the index of refraction is a function of the position