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Han, Shangchen; Liu, Beibei; Cabezas, Randi; Twigg, Christopher D.; Zhang, Peizhao; Petkau, Jeff; Yu, Tsz-Ho; Tai, Chun-Jung; Akbay, Muzaffer; Wang, Zheng; Nitzan, Asaf; Dong, Gang; Ye, Yuting; Tao, Lingling; Wan, Chengde; Wang, Robert
ACM transactions on graphics, 07/2020, Volume: 39, Issue: 4Journal Article
We present a system for real-time hand-tracking to drive virtual and augmented reality (VR/AR) experiences. Using four fisheye monochrome cameras, our system generates accurate and low-jitter 3D hand motion across a large working volume for a diverse set of users. We achieve this by proposing neural network architectures for detecting hands and estimating hand keypoint locations. Our hand detection network robustly handles a variety of real world environments. The keypoint estimation network leverages tracking history to produce spatially and temporally consistent poses. We design scalable, semi-automated mechanisms to collect a large and diverse set of ground truth data using a combination of manual annotation and automated tracking. Additionally, we introduce a detection-by-tracking method that increases smoothness while reducing the computational cost; the optimized system runs at 60Hz on PC and 30Hz on a mobile processor. Together, these contributions yield a practical system for capturing a user's hands and is the default feature on the Oculus Quest VR headset powering input and social presence.
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