Papers › Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses
Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses
Chen Li, Gim Hee Lee
3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. Several previous works addressed the inverse problem by generating multiple hypotheses. However, these works are strongly supervised and require ground truth 2D-to-3D correspondences which can be difficult to obtain. In this paper, we propose a weakly supervised deep generative network to address the inverse problem and circumvent the need for ground truth 2D-to-3D correspondences. To this end, we design our network to model a proposal distribution which we use to approximate the unknown multi-modal target posterior distribution. We achieve the approximation by minimizing the KL divergence between the proposal and target distributions, and this leads to a 2D reprojection error and a prior loss term that can be weakly supervised. Furthermore, we determine the most probable solution as the conditional mode of the samples using the mean-shift algorithm. We evaluate our method on three benchmark datasets -- Human3.6M, MPII and MPI-INF-3DHP. Experimental results show that our approach is capable of generating multiple feasible hypotheses and achieves state-of-the-art results compared to existing weakly supervised approaches. Our source code is available at the project website.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Human Pose Estimation | MPI-INF-3DHP | WSGAN | PCK | 79.3 | #106 of 108 | Archive leaderboard | report |
| Multi-Hypotheses 3D Human Pose Estimation | Human3.6M | Li et al. | Average MPJPE (mm) | 73.9 | #11 of 12 | Archive leaderboard | report |
| Multi-Hypotheses 3D Human Pose Estimation | Human3.6M | Li et al. | Average PMPJPE (mm) | 44.3 | #11 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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