QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars

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.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Character Controllers Using Motion VAEs
• Robust Motion In-betweening
• ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
• MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
• AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing

Cited by in corpus (4)

• HOOV: Hand Out-Of-View Tracking for Proprioceptive Interaction using Inertial Sensing
• QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors
• Animation Fidelity in Self-Avatars: Impact on User Performance and Sense of Agency
• Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions