PoseGU: 3D human pose estimation with novel human pose generator and unbiased learning
- Publisher:
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Publication Type:
- Journal Article
- Citation:
- Computer Vision and Image Understanding, 2023, 233
- Issue Date:
- 2023-08-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Pose GU.pdf | Published version | 6.16 MB |
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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 outperforms 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.
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