Real camera footage is subject to noise, motion blur (MB) and depth of field (DoF). In some applications these might be considered distortions to be removed, but in others it is important to model them because it would be ineffective, or interfere with an aesthetic choice, to simply remove them. In augmented reality applications where virtual content is composed into a live video feed, we can model noise, MB and DoF to make the virtual content visually consistent with the video. Existing methods for this typically suffer two main limitations. First, they require a camera calibration step to relate a known calibration target to the specific cameras response. Second, existing work require methods that can be (differentiably) tuned to the calibration, such as slow and specialized neural networks. We propose a method which estimates parameters for noise, MB and DoF instantly, which allows using off-the-shelf real-time simulation methods from e.g., a game engine in compositing augmented content. Our main idea is to unlock both features by showing how to use modern computer vision methods that can remove noise, MB and DoF from the video stream, essentially providing self-calibration. This allows to auto-tune any black-box real-time nose+MB-DoF method to deliver fast and high-fidelity augmentation consistency.

Remote collaborative work has become pervasive in many settings, ranging from engineering to medical professions. Users are im-mersed in virtual environments and communicate through life-sized avatars that enable face-to-face collaboration. Within this context, users often collaboratively view and interact with virtual 3D models, for example to assist in the design of new devices such as cus-tomized prosthetics, vehicles or buildings. Discussing such shared 3D content face-to-face, however, has a variety of challenges such as ambiguities, occlusions, and different viewpoints that all decrease mutual awareness, which in turn leads to decreased task performance and increased errors. To address this challenge, we introduce MAGIC, a novel approach for understanding pointing gestures in a face-to-face shared 3D space, improving mutual understanding and awareness. Our approach distorts the remote user's gestures to correctly reflect them in the local user's reference space when face-to-face. To measure what two users perceive in common when using pointing gestures in a shared 3D space, we introduce a novel metric called pointing agreement. Results from a user study suggest that MAGIC significantly improves pointing agreement in face-to-face collaboration settings, improving co-presence and awareness of interactions performed in the shared space. We believe that MAGIC improves remote collaboration by enabling simpler communication mechanisms and better mutual awareness.

Highly complex and dense models of 3D objects have recently become indispensable in digital industries. Mesh decimation then plays a crucial role in the production pipeline to efficiently get visually convincing yet compact expressions of complex meshes. However, the current pipeline typically does not allow artists control the decimation process, just a simplification rate. Thus a preferred approach in production settings splits the process into a first pass of saliency detection highlighting areas of greater detail, and allowing artists to iterate until satisfied before simplifying the model. We propose a novel, efficient multi-scale method to compute mesh saliency at coarse and finer scales, based on fast mesh entropy of local surface measurements. Unlike previous approaches, we ensure a robust and straightforward calculation of mesh saliency even for densely tessellated models with millions of polygons. Moreover, we introduce a new adaptive subsampling and interpolation algorithm for saliency estimation. Our implementation achieves speedups of up to three orders of magnitude over prior approaches. Experimental results showcase its resilience to problem scenarios that efficiently scales up to process multi-million vertex meshes. Our evaluation with artists in the entertainment industry also demonstrates its applicability to real use-case scenarios.

Image-warping, a per-pixel deformation of one image into another, is an essential component in immersive visual experiences such as virtual reality or augmented reality. The primary issue with image warping is disocclusions, where occluded (and hence unknown) parts of the input image would be required to compose the output image. We introduce a new image warping method, Metameric image inpainting - an approach for hole-filling in real-time with foundations in human visual perception. Our method estimates image feature statistics of disoccluded regions from their neighbours. These statistics are inpainted and used to synthesise visuals in real-time that are less noticeable to study participants, particularly in peripheral vision. Our method offers speed improvements over the standard structured image inpainting methods while improving realism over colour-based inpainting such as push-pull. Hence, our work paves the way towards future applications such as depth image-based rendering, 6-DoF 360 rendering, and remote render-streaming.

Computer-Generated Holography (CGH) offers the potential for genuine, high-quality three-dimensional visuals. However, fulfilling this potential remains a practical challenge due to computational complexity and visual quality issues. We propose a new CGH method that exploits gaze-contingency and perceptual graphics to accelerate the development of practical holographic display systems. Firstly, our method infers the user’s focal depth and generates images only at their focus plane without using any moving parts. Second, the images displayed are metamers; in the user’s peripheral vision, they need only be statistically correct and blend with the fovea seamlessly. Unlike previous methods, our method prioritises and improves foveal visual quality without causing perceptually visible distortions at the periphery. To enable our method, we introduce a novel metameric loss function that robustly compares the statistics of two given images for a known gaze location. In parallel, we implement a model representing the relation between holograms and their image reconstructions. We couple our differentiable loss function and model to metameric varifocal holograms using a stochastic gradient descent solver. We evaluate our method with an actual proof-of-concept holographic display, and we show that our CGH method leads to practical and perceptually three-dimensional image reconstructions.

Head-Mounted Virtual reality (VR) systems provide full-immersive experiences to users and completely isolate them from the outside world, placing them in unsafe situations. Existing research proposed different alert-based solutions to address this. Our work builds on these studies on notification systems for VR environments from a different perspective. We focus on: (i) exploring alert systems to notify VR users about non-immersed bystanders' in socially related, non-critical interaction contexts; (ii) understanding how best to provide awareness of non-immersed bystanders while maintaining presence and immersion within the Virtual Environment(VE). To this end, we developed single and combined alert cues - leveraging proxemics, perception channels, and push/pull approaches and evaluated those via two user studies. Our findings indicate a strong preference towards maintaining immersion and combining audio and visual cues, push and pull notification techniques that evolve dynamically based on proximity.

In this paper, we present a system that allows a user with a head-mounted display (HMD) to communicate and collaborate with spectators outside of the headset. We evaluate its impact on task performance, immersion, and collaborative interaction. Our solution targets scenarios like live presentations or multi-user collaborative systems, where it is not convenient to develop a VR multiplayer experience and supply each user (and spectator) with an HMD. The spectator views the virtual world on a large-scale tiled video wall and is given the ability to control the orientation of their own virtual camera. This allows spectators to stay focused on the immersed user's point of view or freely look around the environment. To improve collaboration between users, we implemented a pointing system where a spectator can point at objects on the screen, which maps an indicator directly onto the objects in the virtual world. We conducted a user study to investigate the influence of rotational camera decoupling and pointing gestures in the context of HMD-immersed and non-immersed users utilizing a large-scale display. Our results indicate that camera decoupling and pointing positively impacts collaboration. A decoupled view is preferable in situations where both users need to indicate objects of interest in the scene, such as presentations and joint-task scenarios, as it requires a shared reference space. A coupled view, on the other hand, is preferable in synchronous interactions such as remote-assistant scenarios.

To peripheral vision, a pair of physically different images can look the same. Such pairs are metamers relative to each other, just as physically-different spectra of light are perceived as the same color. We propose a real-time method to compute such ventral metamers for foveated rendering where, in particular for near-eye displays, the largest part of the framebuffer maps to the periphery. This improves in quality over state-of-the-art foveation methods which blur the periphery. Work in Vision Science has established how peripheral stimuli are ventral metamers if their statistics are similar. Existing methods, however, require a costly optimization process to find such metamers. To this end, we propose a novel type of statistics particularly well-suited for practical real-time rendering: smooth moments of steerable filter responses. These can be extracted from images in time constant in the number of pixels and in parallel over all pixels using a GPU. Further, we show that they can be compressed effectively and transmitted at low bandwidth. Finally, computing realizations of those statistics can again be performed in constant time and in parallel. This enables a new level of quality for foveated applications such as such as remote rendering, level-of-detail and Monte-Carlo denoising. In a user study, we finally show how human task performance increases and foveation artifacts are less suspicious, when using our method compared to common blurring.

Telecollaboration involves the teleportation of a remote collaborator to another real-world environment where their partner is located. The fidelity of the environment plays an important role for allowing corresponding spatial references in remote collaboration. We present a novel asymmetric platform, Augmented Virtual Teleportation (AVT), which provides high-fidelity telepresence of a remote VR user (VR-Traveler) into a real-world collaboration space to interact with a local AR user (AR-Host). AVT uses a 360° video camera (360-camera) that captures and live-streams the omni-directional scenes over a network. The remote VR-Traveler watching the video in a VR headset experiences live presence and co-presence in the real-world collaboration space. The VR-Traveler's movements are captured and transmitted to a 3D avatar overlaid onto the 360-camera which can be seen in the AR-Host's display. The visual and audio cues for each collaborator are synchronized in the Mixed Reality Collaboration space (MRC-space), where they can interactively edit virtual objects and collaborate in the real environment using the real objects as a reference. High fidelity, real-time rendering of virtual objects and seamless blending into the real scene allows for unique mixed reality use-case scenarios. Our working prototype has been tested with a user study to evaluate spatial presence, co-presence, and user satisfaction during telecollaboration. Possible applications of AVT are identified and proposed to guide future usage.

When engaged in communication, people often rely on pointing gestures to refer to out-of-reach content. However, observers frequently misinterpret the target of a pointing gesture. Previous research suggests that to perform a pointing gesture, people place the index finger on or close to a line connecting the eye to the referent, while observers interpret pointing gestures by extrapolating the referent using a vector defined by the arm and index finger. In this paper we present Warping Deixis, a novel approach to improving the perception of pointing gestures and facilitate communication in collaborative Extended Reality environments. By warping the virtual representation of the pointing individual, we are able to match the pointing expression to the observer's perception. We evaluated our approach in a co-located side by side virtual reality scenario. Results suggest that our approach is effective in improving the interpretation of pointing gestures in shared virtual environments.

Modern volumetric projection-based telepresence approaches are capable of providing realistic full-size virtual representations of remote people. Interacting with full-size people may not be desirable due to the spatial constraints of the physical environment, application context, or display technology. However, the miniaturization of remote people is known to create an eye gaze matching problem. Eye-contact is essential to communication as it allows for people to use natural nonverbal cues and improves the sense of “being there”. In this paper we discuss the design space for interacting with volumetric representations of people and present an approach for dynamically manipulating scale, orientation and the position of holograms which guarantees eye-contact. We created a working augmented reality-based prototype and validated it with 14 participants.

A clear and well-documented LaTeX document is presented as an article formatted for publication by ACM in a conference proceedings or journal publication. Based on the “acmart” document class, this article presents and explains many of the common variations, as well as many of the formatting elements an author may use in the preparation of the documentation of their work.

Head-Mounted Displays are useful to place users in virtual reality (VR). They do this by totally occluding the physical world, including users' bodies. This can make self-awareness problematic. Indeed, researchers have shown that users' feeling of presence and spatial awareness are highly influenced by their virtual representations, and that self-embodied representations (avatars) of their anatomy can make the experience more engaging. On the other hand, recent user studies show a penchant towards a third-person view of one's own body to seemingly improve spatial awareness. However, due to its unnaturality, we argue that a third-person perspective is not as effective or convenient as a first-person view for task execution in VR. In this paper, we investigate, through a user evaluation, how these perspectives affect task performance and embodiment, focusing on navigation tasks, namely walking while avoiding obstacles. For each perspective, we also compare three different levels of realism for users' representation, specifically a stylized abstract avatar, a mesh-based generic human, and a real-time point-cloud rendering of the users' own body. Our results show that only when a third-person perspective is coupled with a realistic representation, a similar sense of embodiment and spatial awareness is felt. In all other cases, a first-person perspective is still better suited for navigation tasks, regardless of representation.

Current state-of-the-art point cloud visualization techniques have shortcomings when dealing with sparse and less accurate data or close-up interactions. In this paper, we present a visualization technique called stroke-based splatting, which applies concepts of stroke-based rendering to surface-aligned splatting, allowing for better shape perception at lower resolutions and close-ups. We create a painterly depiction of the data with an impressionistic aesthetic, which is a metaphor the user is culturally trained to recognize, thus attributing higher quality to the visualization. This is achieved by shaping each object-aligned splat as a brush stroke, and orienting it according to globally coherent tangent vectors from the Householder formula, creating a painterly depiction of the scanned cloud. Each splat is sized according to a color-based clustering analysis of the data, ensuring the consistency of brush strokes within neighborhood areas. By controlling brush shape generation parameters and blending factors between neighboring splats, the user is able to simulate different painting styles in real time. We have tested our method with data sets captured by commodity laser scanners as well as publicly available high-resolution point clouds, both having highly interactive frame rates in all cases. In addition, a user study was conducted comparing our approach to state-of-the-art point cloud visualization techniques. Users considered stroke-based splatting a valuable technique as it provides a higher or similar visual quality to current approaches.

Context-aware pervasive applications can improve user experiences by tracking people in their surroundings. Such systems use multiple sensors to gather information regarding people and devices. However, when developing novel user experiences, researchers are left to building foundation code to support multiple network-connected sensors, a major hurdle to rapidly developing and testing new ideas.