Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

TL;DR

Sparse Inertial Poser (SIP) enables accurate 3D human pose estimation in the wild using only six inertial sensors by leveraging a statistical body model and joint optimization, outperforming previous methods without video input.

Contribution

The paper introduces SIP, a novel framework that estimates 3D human pose with only six inertial sensors by combining a realistic body model and joint optimization, reducing sensor count and avoiding video reliance.

Findings

SIP outperforms dataset baseline accuracy with only 6 sensors.

SIP successfully captures complex outdoor motions like climbing and jumping.

The approach works effectively for arbitrary human motions in real-world scenarios.

Abstract

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.