AR/VR
Computer Vision
MEgATrack: Monochrome Egocentric Articulated Hand-Tracking for Virtual Reality
August 17, 2020
Abstract
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.
AUTHORS
Written by
Shangchen Han
Beibei Liu
Randi Cabezas
Christopher D. Twigg
Peizhao Zhang
Jeff Petkau
Tsz-Ho Yu
Chun-Jung Tai
Muzaffer Akbay
Zheng Wang
Asaf Nitzan
Gang Dong
Yuting Ye
Lingling Tao
Chengde Wan
Robert Wang
Publisher
SIGGRAPH 2020
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