Estimating Egocentric 3D Human Pose in Global Space

TL;DR

This paper introduces a novel method for estimating globally accurate 3D human poses from a single egocentric fisheye camera, overcoming local coordinate limitations and improving stability and accuracy in unconstrained environments.

Contribution

The proposed approach enables global 3D pose estimation from a single head-mounted fisheye camera using spatio-temporal optimization with learned motion priors.

Findings

Outperforms state-of-the-art methods quantitatively.

Provides more accurate and stable global 3D poses.

Effective in unconstrained daily activity environments.

Abstract

Egocentric 3D human pose estimation using a single fisheye camera has become popular recently as it allows capturing a wide range of daily activities in unconstrained environments, which is difficult for traditional outside-in motion capture with external cameras. However, existing methods have several limitations. A prominent problem is that the estimated poses lie in the local coordinate system of the fisheye camera, rather than in the world coordinate system, which is restrictive for many applications. Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective. To tackle these limitations, we present a new method for egocentric global 3D body pose estimation using a single head-mounted fisheye camera. To achieve accurate and temporally stable global poses, a spatio-temporal optimization is performed over a sequence of frames by minimizing heatmap reprojection errors and enforcing local and global body motion priors learned from a mocap dataset. Experimental results show that our approach outperforms state-of-the-art methods both quantitatively and qualitatively.