Physics-Guided Human Motion Capture with Pose Probability Modeling
Jingyi Ju Buzhen Huang Chen Zhu Zhihao Li Yangang Wang
Incorporating physics in human motion capture to avoid artifacts like
floating, foot sliding, and ground penetration is a promising direction.
Existing solutions always adopt kinematic results as reference motions, and the
physics is treated as a post-processing module. However, due to the depth
ambiguity, monocular motion capture inevitably suffers from noises, and the
noisy reference often leads to failure for physics-based tracking. To address
the obstacles, our key-idea is to employ physics as denoising guidance in the
reverse diffusion process to reconstruct physically plausible human motion from
a modeled pose probability distribution. Specifically, we first train a latent
gaussian model that encodes the uncertainty of 2D-to-3D lifting to facilitate
reverse diffusion. Then, a physics module is constructed to track the motion
sampled from the distribution. The discrepancies between the tracked motion and
image observation are used to provide explicit guidance for the reverse
diffusion model to refine the motion. With several iterations, the
physics-based tracking and kinematic denoising promote each other to generate a
physically plausible human motion. Experimental results show that our method
outperforms previous physics-based methods in both joint accuracy and success
rate. More information can be found at
\url{https://github.com/Me-Ditto/Physics-Guided-Mocap}.