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IEEE Annual International Symposium Virtual Reality

作者： Duotun Wang James Kubrlcht Yixin Zhu Wei Lianq Song-Chun Zhu Chenfanfu Jiang Hongjing Lu Laboratory of Intelligent Information Technology Beijing Institute of Technology UCLA Computational Vision and Learning Laboratory Cognition Center for Vision Computer Graphics Group UPenn

ISBN: (纸本)9781538633663

When a moving object collides with an object at rest, people immediately perceive a causal event: i.e., the first object has launched the second object forwards. However, when the second object's motion is delayed, or is accompanied by a collision sound, causal impressions attenuate and strengthen. Despite a rich literature on causal perception, researchers have exclusively utilized 2D visual displays to examine the launching effect. It remains unclear whether people are equally sensitive to the spatiotemporal properties of observed collisions in the real world. The present study first examined whether previous findings in causal perception with audiovisual inputs can be extended to immersive 3D virtual environments. We then investigated whether perceived causality is influenced by variations in the spatial position of an auditory collision indicator. We found that people are able to localize sound positions based on auditory inputs in VR environments, and spatial discrepancy between the estimated position of the collision sound and the visually observed impact location attenuates perceived causality.

关键词： Three-dimensional displays Visualization Solid modeling Delays Spatiotemporal phenomena Virtual environments

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Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars

arXiv

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arXiv 2017年

作者： Jiang, Chenfanfu Qi, Siyuan Zhu, Yixin Huang, Siyuan Lin, Jenny Yu, Lap-Fai Terzopoulos, Demetri Zhu, Song-Chun SIG Center for Computer Graphics University of Pennsylvania UCLA Center for Vision Cognition Learning and Autonomy University of California Los Angeles United States Graphics and Virtual Environments Laboratory University of Massachusetts Boston UCLA Computer Graphics & Vision Laboratory University of California Los Angeles United States

We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms. In particular, we devise a learning-based pipeline of algorithms capable of automatically generating and rendering a potentially infinite variety of indoor scenes by using a stochastic grammar, represented as an attributed Spatial And-Or Graph, in conjunction with state-of-the-art physics-based rendering. Our pipeline is capable of synthesizing scene layouts with high diversity, and it is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. It renders photorealistic RGB images of the generated scenes while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity, and material information (detailed to object parts), as well as environments (e.g., illuminations and camera viewpoints). We demonstrate the value of our synthesized dataset, by improving performance in certain machine-learning-based scene understanding tasks—depth and surface normal prediction, semantic segmentation, reconstruction, etc.—and by providing benchmarks for and diagnostics of trained models by modifying object attributes and scene properties in a controllable manner. Copyright © 2017, The Authors. All rights reserved.

关键词： Pixels

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Evaluating physical quantities and learning human utilities from RGBD videos 16

Evaluating physical quantities and learning human utilities ...

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2016 SIGGRAPH ASIA Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments, SA 2016

作者： Zhu, Yixin Jiang, Chenfanfu Zhao, Yibiao Terzopoulos, Demetri Zhu, Song-Chun UCLA Center for Vision Cognition Learning and Autonomy United States UCLA Computer Graphics and Vision Laboratory United States

ISBN: (纸本)9781450345484

We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects. Rather than defining affordance in terms of the geometric compatibility between body poses and 3D objects, we devise algorithms that employ physics-based simulation to infer the relevant forces/pressures acting on body parts. By observing the choices people make in videos (particularly in selecting a chair in which to sit) our system learns the comfort intervals of the forces exerted on body parts (while sitting). We account for people's preferences in terms of human utilities, which transcend comfort intervals to account also for meaningful tasks within scenes and spatiotemporal constraints in motion planning, such as for the purposes of robot task planning. © 2016 Copyright held by the owner/author(s).SA '16, December 05-08 2016.

关键词： Motion planning

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Inferring forces and learning human utilities from videos

Inferring forces and learning human utilities from videos

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2016 IEEE Conference on computer vision and Pattern Recognition, CVPR 2016

ISBN: (纸本)9781467388511

We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects. Rather than defining affordance in terms of the geometric compatibility between body poses and 3D objects, we devise algorithms that employ physicsbased simulation to infer the relevant forces/pressures acting on body parts. By observing the choices people make in videos (particularly in selecting a chair in which to sit) our system learns the comfort intervals of the forces exerted on body parts (while sitting). We account for people's preferences in terms of human utilities, which transcend comfort intervals to account also for meaningful tasks within scenes and spatiotemporal constraints in motion planning, such as for the purposes of robot task planning.

关键词： Robot programming

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A virtual reality platform for dynamic human-scene interaction 16

A virtual reality platform for dynamic human-scene interacti...

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2016 SIGGRAPH ASIA Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments, SA 2016

作者： Lin, Jenny Guo, Xingwen Shao, Jingyu Jiang, Chenfanfu Zhu, Yixin Zhu, Song-Chun UCLA Center for Vision Cognition Learning and Autonomy United States University of Hong Kong Electrical and Electronic Engineering Department Hong Kong UCLA Computer Graphics and Vision Laboratory United States

ISBN: (纸本)9781450345484

Both synthetic static and simulated dynamic 3D scene data is highly useful in the fields of computer vision and robot task planning. Yet their virtual nature makes it difficult for real agents to interact with such data in an intuitive way. Thus currently available datasets are either static or greatly simplified in terms of interactions and dynamics. In this paper, we propose a system in which Virtual Reality and human/finger pose tracking is integrated to allow agents to interact with virtual environments in real time. Segmented object and scene data is used to construct a scene within Unreal Engine 4, a physics-based game engine. We then use the Oculus Rift headset with a Kinect sensor, Leap Motion controller and a dance pad to navigate and manipulate objects inside synthetic scenes in real time. We demonstrate how our system can be used to construct a multi-jointed agent representation as well as fine-grained finger pose. In the end, we propose how our system can be used for robot task planning and image semantic segmentation. © 2016 Copyright held by the owner/author(s). SA '16, December 05-08 2016.

关键词： Virtual reality

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Inferring Forces and Learning Human Utilities From Videos

Inferring Forces and Learning Human Utilities From Videos

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IEEE Conference on computer vision and Pattern Recognition

作者： Yixin Zhu Chenfanfu Jiang Yibiao Zhao Demetri Terzopoulos Song-Chun Zhu UCLA Center for Vision Cognition Learning and Autonomy UCLA Computer Graphics & Vision Laboratory

ISBN: (纸本)9781467388528

关键词： Three-dimensional displays Solid modeling Finite element analysis Planning Robots Biological system modeling Videos

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