TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors

TL;DR

TransPose is a real-time deep learning system that accurately captures full 3D human motion, including global translation and pose, using only six inertial sensors, overcoming occlusion and environmental limitations of vision-based methods.

Contribution

The paper introduces a novel multi-stage neural network and fusion techniques for accurate, efficient 3D human motion capture from minimal inertial sensors, outperforming existing methods.

Findings

Outperforms state-of-the-art in accuracy and efficiency

Achieves over 90 fps in real-time processing

Robustly estimates global translation without environmental constraints

Abstract

Motion capture is facing some new possibilities brought by the inertial sensing technologies which do not suffer from occlusion or wide-range recordings as vision-based solutions do. However, as the recorded signals are sparse and quite noisy, online performance and global translation estimation turn out to be two key difficulties. In this paper, we present TransPose, a DNN-based approach to perform full motion capture (with both global translations and body poses) from only 6 Inertial Measurement Units (IMUs) at over 90 fps. For body pose estimation, we propose a multi-stage network that estimates leaf-to-full joint positions as intermediate results. This design makes the pose estimation much easier, and thus achieves both better accuracy and lower computation cost. For global translation estimation, we propose a supporting-foot-based method and an RNN-based method to robustly solve for the global translations with a confidence-based fusion technique. Quantitative and qualitative comparisons show that our method outperforms the state-of-the-art learning- and optimization-based methods with a large margin in both accuracy and efficiency. As a purely inertial sensor-based approach, our method is not limited by environmental settings (e.g., fixed cameras), making the capture free from common difficulties such as wide-range motion space and strong occlusion.