EgoMDM: Diffusion-Based Human Motion Synthesis from Sparse Egocentric Sensors

Accurate three-dimensional (3D) human motion tracking is essential for immersive augmented reality$(A R)$and virtual reality (VR) applications, allowing users to engage with virtual environments through realistic full-body avatars. Achieving this level of detail, however, is challenging when the driving signals are sparse, typically coming only from upper-body sensors, such as head-mounted devices and hand controllers. To address this challenge, we propose EgoMDM (Egocentric Motion Diffusion Model), an end-to-end diffusion-based framework designed to reconstruct full-body motion from sparse tracking signals. EgoMDM models human motion in a conditional autoregressive manner using a unidirectional recurrent neural network, making it well-suited for real-time applications. By embedding local-to-global translation, forward and inverse kinematics, and foot-contact detection within the diffusion framework, EgoMDM achieves seamless, end-to-end motion synthesis, effectively reducing artifacts like foot sliding and ground penetration. Additionally, EgoMDM is conditioned on the user's body scale, allowing it to generalize across a diverse population and produce consistent avatar shapes over time. In our extensive experiments on the AMASS motion capture dataset, EgoMDM achieves state-of-the-art performance in both motion tracking accuracy and synthesis quality, demonstrating its robustness and adaptability across various human motion scenarios. Furthermore, EgoMDM significantly outperforms the existing models when tested on real signal inputs, highlighting its robustness and applicability to the real-world data. See the project page at: https://yohanshin.github.io/egomdm.github.io/