GFPose: Learning 3D Human Pose Prior with Gradient Fields
1. Beijing Institute for General Artificial Intelligence 2. Peking University
GFPose is a score-based 3D human pose prior model that can be easily used for various applications, e.g., 3D human pose estimation, pose denoising and generation. Our key idea is to estimate the gradient field (a.k.a, score) of the perturbed human pose. Scores encode "what a reasonable pose looks like." We can leverage them to adjust poses to be more plausible and feasible to a task specification.
Multi-hypothesis 3D Human Pose Estimation
web_hpe.mp4
Qualitative comparison with cVAE
We compare the hypotheses sampled from GFPose and cVAE[1]. Preds and GTs are colored yellow and white, respectively.
Both methods work well with the easier case (3rd row). However, GFPose performs better with the harder ones (first two rows). It gives more diverse hypotheses while keeping faithful to GT.
[1] Saurabh Sharma, et al. Monocular 3d human pose estimation by generation and ordinal ranking. In ICCV, 2019
Reconstruction from Occluded 2D Observation
web_completion2D.mp4Reconstruction from Partial 3D Observation
web_completion3D.mp4Pose Denoising
web_denoising.mp4Pose Generation
web_generation.mp4Citation
@article{ci2022gfpose,
title = {GFPose: Learning 3D Human Pose Prior with Gradient Fields},
author = {Ci, Hai and Wu, Mingdong and Zhu, Wentao and Ma, Xiaoxuan and Dong, Hao and Zhong, Fangwei and Wang, Yizhou},
journal = {arXiv preprint arXiv:2212.08641},
year = {2022}
}