UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality

• URL: http://arxiv.org/abs/2012.03680v1
• Date: Thu, 12 Nov 2020 09:31:09 GMT
• Title: UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality
• Authors: Mathias Parger, Chengcheng Tang, Yuanlu Xu, Christopher Twigg, Lingling Tao, Yijing Li, Robert Wang, and Markus Steinberger
• Abstract summary: In this paper, we propose a new data-driven framework for inside-out body tracking. We first collect a large-scale motion capture dataset with both body and finger motions. We then simulate the occlusion patterns in head-mounted camera views on the captured ground truth using a ray casting algorithm and learn a deep neural network to infer the occluded body parts.
• Score: 12.349749717823736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments. Unlike the popular 3D pose estimation setting, the problem is often formulated as inside-out tracking based on embodied perception (e.g., egocentric cameras, handheld sensors). In this paper, we propose a new data-driven framework for inside-out body tracking, targeting challenges of omnipresent occlusions in optimization-based methods (e.g., inverse kinematics solvers). We first collect a large-scale motion capture dataset with both body and finger motions using optical markers and inertial sensors. This dataset focuses on social scenarios and captures ground truth poses under self-occlusions and body-hand interactions. We then simulate the occlusion patterns in head-mounted camera views on the captured ground truth using a ray casting algorithm and learn a deep neural network to infer the occluded body parts. In the experiments, we show that our method is able to generate high-fidelity embodied poses by applying the proposed method on the task of real-time inside-out body tracking, finger motion synthesis, and 3-point inverse kinematics.

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