Papers › Cascaded deep monocular 3D human pose estimation with evolutionary training data

Cascaded deep monocular 3D human pose estimation with evolutionary training data

14 Jun 2020CVPR 2020 6arXiv:2006.07778archive 2025-07-28

Shichao Li, Lei Ke, Kevin Pratama, Yu-Wing Tai, Chi-Keung Tang, Kwang-Ting Cheng

End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data. This paper proposes a novel data augmentation method that: (1) is scalable for synthesizing massive amount of training data (over 8 million valid 3D human poses with corresponding 2D projections) for training 2D-to-3D networks, (2) can effectively reduce dataset bias. Our method evolves a limited dataset to synthesize unseen 3D human skeletons based on a hierarchical human representation and heuristics inspired by prior knowledge. Extensive experiments show that our approach not only achieves state-of-the-art accuracy on the largest public benchmark, but also generalizes significantly better to unseen and rare poses. Code, pre-trained models and tools are available at this HTTPS URL.

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Nicholasli1995/EvoSkeleton officialmentioned in papermentioned on GitHubpytorch report

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Tasks

3D Human Pose EstimationData AugmentationMonocular 3D Human Pose EstimationPose EstimationRepresentation LearningWeakly-supervised 3D Human Pose Estimation

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation Human3.6M TAG-Net Average MPJPE (mm) 50.9 #70 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M TAG-Net Multi-View or Monocular Monocular #70 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M TAG-Net Using 2D ground-truth joints No #70 of 88 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP EvoSkeleton AUC 46.1 #68 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP EvoSkeleton MPJPE 99.7 #68 of 108 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP EvoSkeleton PCK 81.2 #68 of 108 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TAG-Net Average MPJPE (mm) 50.9 #24 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TAG-Net Frames Needed 1 #24 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TAG-Net Need Ground Truth 2D Pose No #24 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TAG-Net Use Video Sequence No #24 of 52 Archive leaderboard report
Weakly-supervised 3D Human Pose Estimation Human3.6M Li et al. 3D Annotations S1 #13 of 33 Archive leaderboard report
Weakly-supervised 3D Human Pose Estimation Human3.6M Li et al. Average MPJPE (mm) 62.9 #13 of 33 Archive leaderboard report
Weakly-supervised 3D Human Pose Estimation Human3.6M Li et al. Number of Frames Per View 1 #13 of 33 Archive leaderboard report
Weakly-supervised 3D Human Pose Estimation Human3.6M Li et al. Number of Views 1 #13 of 33 Archive leaderboard report

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