Improving Global Motion Estimation in Sparse IMU-based Motion Capture with Physics

TL;DR

This paper introduces a physics-based optimization approach to improve global motion estimation in sparse IMU-based motion capture, enhancing accuracy and enabling estimation of contact forces and joint torques.

Contribution

It presents a novel physics-informed optimization scheme that improves global translation and orientation estimation in IMU-based motion capture systems.

Findings

Enhanced accuracy in local pose estimation.

Improved global motion reconstruction.

Successful estimation of contact forces and joint torques.

Abstract

By learning human motion priors, motion capture can be achieved by 6 inertial measurement units (IMUs) in recent years with the development of deep learning techniques, even though the sensor inputs are sparse and noisy. However, human global motions are still challenging to be reconstructed by IMUs. This paper aims to solve this problem by involving physics. It proposes a physical optimization scheme based on multiple contacts to enable physically plausible translation estimation in the full 3D space where the z-directional motion is usually challenging for previous works. It also considers gravity in local pose estimation which well constrains human global orientations and refines local pose estimation in a joint estimation manner. Experiments demonstrate that our method achieves more accurate motion capture for both local poses and global motions. Furthermore, by deeply integrating physics, we can also estimate 3D contact, contact forces, joint torques, and interacting proxy surfaces.