PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning. (arXiv:2207.03618v1 [cs.CV])

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 …

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