Estimating Egocentric 3D Human Pose in Global Space

Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Christian Theobalt; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11500-11509


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.

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@InProceedings{Wang_2021_ICCV, author = {Wang, Jian and Liu, Lingjie and Xu, Weipeng and Sarkar, Kripasindhu and Theobalt, Christian}, title = {Estimating Egocentric 3D Human Pose in Global Space}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {11500-11509} }