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QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars

Cited 18 time in Web of Science Cited 25 time in Scopus
Authors

Winkler, Alexander; Won, Jungdam; Ye, Yuting

Issue Date
2022
Publisher
ASSOC COMPUTING MACHINERY
Citation
PROCEEDINGS SIGGRAPH ASIA 2022, p. 2
Abstract
Real-time tracking of human body motion is crucial for interactive and immersive experiences in AR/VR. However, very limited sensor data about the body is available from standalone wearable devices such as HMDs (Head Mounted Devices) or AR glasses. In this work, we present a reinforcement learning framework that takes in sparse signals from an HMD and two controllers, and simulates plausible and physically valid full body motions. Using high quality full body motion as dense supervision during training, a simple policy network can learn to output appropriate torques for the character to balance, walk, and jog, while closely following the input signals. Our results demonstrate surprisingly similar leg motions to ground truth without any observations of the lower body, even when the input is only the 6D transformations of the HMD. We also show that a single policy can be robust to diverse locomotion styles, different body sizes, and novel environments.
URI
https://hdl.handle.net/10371/201174
DOI
https://doi.org/10.1145/3550469.3555411
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