Aligned color and depth images allow exploring a scene by projecting the virtual camera to arbitrary positions. Depth-image-based rendering (DIBR) methods are often adopted in practical solutions, requiring fewer data than other approaches that explore stereo or multi-view setups. As the virtual camera moves, artifacts are expected to appear, which must be tackled adequately. For video sequences, not only spatial but also temporal inconsistencies affect the user experience. This paper proposes a background model that can be coupled to reduce the flickering of state-of-the-art still-image DIBR methods applied to video sequences captured by static cameras. Qualitative and quantitative results show the potential of the use of our method in practical applications.

Hair rendering has been a focal point of attention in computer graphics for the last couple of decades. However, there have been few contributions to the modeling and rendering of the natural hair aging phenomenon. We present a new technique that simulates the process of hair graying and hair thinning on digital models due to aging. Given a 3D human head model with hair, we first compute a segmentation of the head using K-means since hair aging occurs at different rates in distinct head parts. Hair graying is simulated according to recent biological knowledge on aging factors for hairs, and hair thinning decreases hair diameters linearly with time. Our system is biologically inspired, supports facial hair, both genders and many ethnicities, and is compatible with different lengths of hair strands. Our real-time results resemble real-life hair aging, accomplished by simulating the stochastic nature of the process and the gradual decrease of melanin.

View synthesis allows observers to explore static scenes using aligned color images and depth maps captured in a preset camera path. Among the options, depth-image-based rendering (DIBR) approaches have been effective and efficient since only one pair of color and depth map is required, saving storage and bandwidth. The present work proposes a novel DIBR pipeline for view synthesis that properly tackles the different artifacts that arise from 3D warping, such as cracks, disocclusions, ghosts, and out-of-field areas. A key aspect of our contributions relies on the adaptation and usage of a hierarchical image superpixel algorithm that helps to maintain structural characteristics of the scene during image reconstruction. We compare our approach with state-of-the-art methods and show that it attains the best average results in two common assessment metrics under public still-image and video-sequence datasets. Visual results are also provided, illustrating the potential of our technique in real-world applications.

Whenever accessing indoor spaces such as classrooms or auditoriums, people might attempt to analyze and choose an appropriate place to stay while attending an event. Several criteria may be accounted for, and most are not always self-evident or trivial. This work proposes the use of data visualization allied to an Augmented Reality (AR) user interface to help users defining the most convenient seats to take. We consider sets of arbitrary demands and project information directly atop the seats and all around the room. Users can also narrow down the search by switching and combining the attributes being displayed, e.g., temperature, wheelchair accessibility. The proposed approach was tested against a comparable 2D interactive visualization of the same data in usability assessments of seat-choosing tasks with a set of users (N = 16) to validate the solution. Qualitative and quantitative data indicated that the AR-based solution is promising, suggesting that AR may help users make more accurate decisions, even in an ordinary daily task. Regarding Augmented Situated Visualization, our results open new avenues for the exploration of context-aware data.

Previous research in pattern formation using reaction-diffusion mostly focused on static domains, either for computational simplicity or mathematical tractability. In this work, we have explored the expressiveness of combining simple mechanisms as a possible explanation for pigmentation pattern formation, where tissue growth plays a crucial role. Our motivation is not only to realistically reproduce natural patterns but also to get insights into the underlying biological processes. Therefore, we present a novel approach to generate realistic animal skin patterns. First, we describe the approximation of tissue growth by a series of discrete matrix expansion operations. Then, we combine it with an adaptation of Turing's non-linear reaction-diffusion model, which enforces upper and lower bounds to the concentrations of the involved chemical reagents. We also propose the addition of a single-reagent continuous autocatalytic reaction, called reinforcement, to provide a mechanism to maintain an already established pattern during growth. By careful adjustment of the parameters and the sequencing of operations, we closely match the appearance of a few real species. In particular, we reproduce in detail the distinctive features of the leopard skin, also providing a hypothesis for the simultaneous productions of the most common melanin types, eumelanin and pheomelanin.

In this paper we compare the quality of synthesized views produced by four DIBR methods when fed by depth maps estimated by five state-of-the-art stereo matching algorithms. Also, we compute the correlation between four popular metrics for ranking stereo matching algorithms and two metrics commonly used to evaluate synthesized views (PSNR and SSIM) plus one specific for DIBR. Among our findings, we highlight that (i) PSNR and SSIM have a weak correlation with common stereo matching metrics, (ii) using ground-truth depth does not lead necessarily to the best DIBR result; and (iii) estimated depth maps present artifacts that affect differently DIBR methods.

Abstract The visual fidelity of realistic renderings in Computer Graphics depends fundamentally upon how we model the appearance of objects resulting from the interaction between light and matter reaching the eye. In this paper, we survey the research addressing appearance modelling of living human tissue. Among the many classes of natural materials already researched in Computer Graphics, living human tissues such as blood and skin have recently seen an increase in attention from graphics research. There is already an incipient but substantial body of literature on this topic, but we also lack a structured review as presented here. We introduce a classification for the approaches using the four types of human tissues as classifiers. We show a growing trend of solutions that use first principles from Physics and Biology as fundamental knowledge upon which the models are built. The organic quality of visual results provided by these Biophysical approaches is mainly determined by the optical properties of biophysical components interacting with light. Beyond just picture making, these models can be used in predictive simulations, with the potential for impact in many other areas.

Depth-image-based rendering is a popular way to produce content for three-dimensional television and free viewpoint video, allowing the synthesis of numerous viewpoints using a single reference view and its depth map. Due to the synthesis process and nature of the input, artifacts and holes appear, and solving these problems becomes a challenge. In this letter, we propose solutions to remove those artifacts and apply different filling strategies depending on the nature of each hole. Cracks are identified and filled using very local neighborhood information. Regions classified as ghosts are projected to their correct place. The remaining holes are classified as disocclusions or out-of-field areas, and filled with an appropriate adaptation of a popular inpainting method. In both adaptations, patch matching explores the spatial locality concept, using dynamically adaptive patch sizes from the reference image. For disocclusions we propose a filling order using depth and background terms, and a searching process that considers only background patches. We show that our method outperforms several view synthesis methods in the quantitative evaluation, besides presenting consistent visual results for both large baselines and severely occluded scenes.

Abstract Changes in the human pigmentary system can lead to imbalances in the distribution of melanin in the skin resulting in artefacts known as pigmented lesions. Our work takes as departing point biological data regarding human skin, the pigmentary system and the melanocytes life cycle and presents a reaction–diffusion model for the simulation of the shape features of human‐pigmented lesions. The simulation of such disorders has many applications in dermatology, for instance, to assist dermatologists in diagnosis and training related to pigmentation disorders. Our study focuses, however, on applications related to computer graphics. Thus, we also present a method to seamless blend the results of our simulation model in images of healthy human skin. In this context, our model contributes to the generation of more realistic skin textures and therefore more realistic human models. In order to assess the quality of our results, we measured and compared the characteristics of the shape of real and synthesized pigmented lesions. We show that synthesized and real lesions have no statistically significant differences in their shape features. Visually, our results also compare favourably with images of real lesions, being virtually indistinguishable from real images.

One of the biggest challenges in view interpolation is to fill the regions without projective information in the synthesized view. In this paper, we present a new approach that identifies and corrects different types of missing information. In the first stage, we propose a fast solution to tackle the problems of cracks and ghost, common artifacts in the view interpolation process. Then, we complete larger holes by exploring the disparity map as an additional cue to select the best patch in a patch-based inpainting procedure. Our experimental results indicate that we were able to outperform current state of the art hole filling techniques for view interpolation.

Abstract We present a wavelet‐based approach for selecting patches in patch‐based texture synthesis. We randomly select the first block that satisfies a minimum error criterion, computed from the wavelet coefficients (using 1D or 2D wavelets) for the overlapping region. We show that our wavelet‐based approach improves texture synthesis for samples where previous work fails, mainly textures with prominent aligned features. Also, it generates similar quality textures when compared against texture synthesis using feature maps with the advantage that our proposed method uses implicit edge information (since it is embedded in the wavelet coefficients) whereas feature maps rely explicitly on edge features. In previous work, the best patches are selected among all possible using a L2 norm on the RGB or grayscale pixel values of boundary zones. The L2 metric provides the raw pixel‐to‐pixel difference, disregarding relevant image structures — such as edges — that are relevant in the human visual system and therefore on synthesis of new textures.

The giraffe and its patches, the leopard and its spots, the tiger and its stripes are spectacular examples of the integration of a pattern and a body shape. We present an approach that integrates a biologically-plausible pattern generation model, which can effectively deliver a variety of patterns characteristic of mammalian coats, and a body growth and animation system that uses experimental growth data to produce individual bodies and their associated patterns automatically. We use the example of the giraffe to illustrate how our approach takes us from a canonical embryo to a full adult giraffe in a continuous way, with results that are not only realistic looking, but also objectively validated. The flexibility of the approach is demonstrated by examples of big cat patterns, including an interpolation between patterns. The approach also allows a considerable amount of user control to fine-tune the results and to animate the resulting body with the pattern.

While there exist many computer models of animal bodies, as polygonal meshes and parametric surfaces, these are difficult to modify to take growth into account, or to animate. Growth data available from the literature usually is expressed as very sparse measurements over the body at various ages of the animal. We present here basic techniques to transfer growth data to computer models (especially polygonal meshes), which allows animation of the growth as well as animation of the body in the traditional sense. The main technique consists of defining local coordinate systems around the growing parts of the body, each one being transformed according to the relevant growth data while maintaining their relationship with the adjoining parts and the continuity of the surface. The local coordinates also permit ordinary animation mainly as relative rotation such as in articulated objects. We present examples with polygonal models of horses and cows, growth data from same, and motion from Muybridge’s classic photographic data.