QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars (2209.09391v1)

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.

• The paper presents a reinforcement learning framework that synthesizes full-body motion from sparse sensor input using physics simulation.
• It utilizes high-quality motion capture data as dense supervision to achieve accurate lower-body movement estimation without explicit sensor data.
• The approach shows robust performance across varied locomotion styles and avatars, promising enhanced interactive AR/VR applications.

An Analysis of "QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars"

The paper "QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars" introduces an innovative approach to real-time tracking of human motion using sparse data from AR/VR devices. The research addresses a critical challenge in augmented and virtual reality applications, which is the effective tracking of full body human motion with limited sensor input, specifically from head-mounted devices (HMDs) and controllers.

Methodological Framework

The authors present a reinforcement learning (RL) framework that synthesizes plausible full-body human motion from sparse sensor data. Leveraging a physics-based simulation environment integrated with a reinforcement learning approach, the policy network is trained to generate torques that animate a virtual character, allowing it to mimic the wearer's movements. This methodology effectively utilizes sparse signals, such as the position and orientation of a headset and two hand controllers, to reconstruct convincing full-body poses.

One of the paper's significant contributions is the use of high-quality, full-body motion capture data as dense supervision during training. This approach allows the policy network to understand and generalize the relationship between sparse observable data and the complete body kinematics, resulting in surprisingly accurate lower-body motion estimation—even in the absence of explicit lower body sensor data.

Strong Numerical Results and Robustness

The evaluation of the system, as detailed in the paper, demonstrates its effectiveness across a range of scenarios. Notably, the use of a single policy capable of adapting to varied locomotion styles, body sizes, and new environments is a testament to the robustness of the approach. The authors report that the policy network achieves accurate leg motions comparable to ground truth, and they provide quantitative metrics on the inaccuracies in joint positions and the degree of jitter, both of which are key metrics in character animation and motion capture systems.

Theoretical and Practical Implications

Theoretically, the paper demonstrates the viability of employing reinforcement learning to solve complex, under-constrained problems in motion estimation. By constraining solutions to physically valid poses using a physics simulator, the research highlights how RL can be applied beyond traditional domains, addressing challenges associated with sparse input data.

Practically, the implications of this work are significant for the AR/VR field. The ability to generate full-body animations from minimal input signals opens possibilities for enhancing interactive experiences in gaming, virtual meetings, and AR applications. Not needing extensive marker-based motion capture systems minimizes setup complexity and allows greater flexibility and mobility, which are essential for consumer-grade AR/VR solutions.

Future Directions

The research suggests several promising directions for future work. One potential area of development is enhancing the realism of synthesized poses by integrating more sophisticated reward strategies or employing adversarial learning techniques to improve motion quality. Another avenue is reducing the latency inherent in the system's reliance on future input observations, possibly through predictive models that leverage motion data to anticipate user movements. Additionally, exploring ways to robustly generalize across diverse avatars and user sizes could further enhance the versatility of the system, enabling broader adoption across various applications.

In conclusion, "QuestSim" represents a notable advance in human motion capture from sparse sensor data, illustrating the potential of reinforcement learning integrated with physics simulation to address complex challenges in real-time avatar animation. The research sets a foundation for future developments in the field, promising richer and more immersive AR/VR environments.