Notes

Your institutional ability with the Wellbeing Industry along with the reaction to COVID-19 inside a worldwide standpoint.
Image-based relighting, projector compensation and depth/normal reconstruction are three important tasks of projector-camera systems (ProCams) and spatial augmented reality (SAR). Although they share a similar pipeline of finding projector-camera image mappings, in tradition, they are addressed independently, sometimes with different prerequisites, devices and sampling images. In practice, this may be cumbersome for SAR applications to address them one-by-one. In this paper, we propose a novel end-to-end trainable model named DeProCams to explicitly learn the photometric and geometric mappings of ProCams, and once trained, DeProCams can be applied simultaneously to the three tasks. DeProCams explicitly decomposes the projector-camera image mappings into three subprocesses shading attributes estimation, rough direct light estimation and photorealistic neural rendering. A particular challenge addressed by DeProCams is occlusion, for which we exploit epipolar constraint and propose a novel differentiable projector direct light mask. Thus, it can be learned end-to-end along with the other modules. Afterwards, to improve convergence, we apply photometric and geometric constraints such that the intermediate results are plausible. In our experiments, DeProCams shows clear advantages over previous arts with promising quality and meanwhile being fully differentiable. Moreover, by solving the three tasks in a unified model, DeProCams waives the need for additional optical devices, radiometric calibrations and structured light.Shooter bias is the tendency to more quickly shoot at unarmed Black suspects compared to unarmed White suspects. The primary goal of this research was to investigate the efficacy of shooter bias simulation studies in a more realistic immersive virtual scenario instead of the traditional methodologies using desktop computers. In this paper we present results from a user study (N=99) investigating shooter and racial bias in an immersive virtual environment. Our results highlight how racial bias was observed differently in an immersive virtual environment compared to previous desktop-based simulation studies. Latency to shoot, the standard shooter bias measure, was not found to be significantly different between race or socioeconomic status in our more realistic scenarios where participants chose to raise a weapon and pull a trigger. However, more nuanced head and hand motion analysis was able to predict participants' racial shooting accuracy and implicit racism scores. Discussion of how these nuanced measures can be used for detecting behavior changes for body-swap illusions, and implications of this work related to racial justice and police brutality are discussed.Existing near-eye display designs struggle to balance between multiple trade-offs such as form factor, weight, computational requirements, and battery life. These design trade-offs are major obstacles on the path towards an all-day usable near-eye display. In this work, we address these trade-offs by, paradoxically, removing the display from near-eye displays. We present the beaming displays, a new type of near-eye display system that uses a projector and an all passive wearable headset. check details We modify an off-the-shelf projector with additional lenses. We install such a projector to the environment to beam images from a distance to a passive wearable headset. The beaming projection system tracks the current position of a wearable headset to project distortion-free images with correct perspectives. In our system, a wearable headset guides the beamed images to a user's retina, which are then perceived as an augmented scene within a user's field of view. In addition to providing the system design of the beaming display, we provide a physical prototype and show that the beaming display can provide resolutions as high as consumer-level near-eye displays. We also discuss the different aspects of the design space for our proposal.With the rapidly increasing resolutions of 360° cameras, head-mounted displays, and live-streaming services, streaming high-resolution panoramic videos over limited-bandwidth networks is becoming a critical challenge. Foveated video streaming can address this rising challenge in the context of eye-tracking-equipped virtual reality head-mounted displays. However, conventional log-polar foveated rendering suffers from a number of visual artifacts such as aliasing and flickering. In this paper, we introduce a new log-rectilinear transformation that incorporates summed-area table filtering and off-the-shelf video codecs to enable foveated streaming of 360° videos suitable for VR headsets with built-in eye-tracking. To validate our approach, we build a client-server system prototype for streaming 360° videos which leverages parallel algorithms over real-time video transcoding. We conduct quantitative experiments on an existing 360° video dataset and observe that the log-rectilinear transformation paired with summed-area table filtering heavily reduces flickering compared to log-polar subsampling while also yielding an additional 10% reduction in bandwidth usage.Learning an advanced skill in sports requires a huge amount of practice and players also have to overcome both physical difficulties and the dullness of repetitive training. Returning a fast spin shot in table tennis could be taken as an example, as athletes need to judge the spin type and decide the racket pose within a second, which is difficult for beginners. Therefore, in this paper, we show how to design an intuitive training system to acquire this specific skill using different cues in Virtual Reality (VR). Using VR, we can easily provide visual information, attach haptic devices, and distort the speed of time, however, it is difficult to decide which types of information could benefit the training. In an initial study, by comparing real world training with VR training, we showed the effect of VR training and obtained some insights about augmentation for training spin shots. The training system was then improved by adding three new conditions using different visualizations and temporal distortions, as well as a haptic racket for creating realistic feedback.
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