3D pose estimation has recently gained substantial interests in computer vision domain. Existing 3D pose estimation methods have a strong reliance on large size well-annotated 3D pose datasets, and they suffer poor model generalization on unseen poses due to limited diversity of 3D poses in training sets. In this work, we propose PoseGU, a novel human pose generator that generates diverse poses with access only to a small size of seed samples, while equipping the Counterfactual Risk Minimization to pursue an unbiased evaluation objective. Extensive experiments demonstrate PoseGU outforms almost all the state-of-the-art 3D human pose methods under consideration over three popular benchmark datasets. Empirical analysis also proves PoseGU generates 3D poses with improved data diversity and better generalization ability.
@article{arxiv.2207.03618,
title = {PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning},
author = {Shannan Guan and Haiyan Lu and Linchao Zhu and Gengfa Fang},
journal= {arXiv preprint arXiv:2207.03618},
year = {2022}
}