Papers › Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses

Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses

13 Aug 2020arXiv:2008.05770archive 2025-07-28

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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bias_variable chaneyddtt/weakly-supervised-3d-pose-generator/layers.py official repository unverified MIT (permissive) · 9fdd8216a2990a2b · report
err_3dpe chaneyddtt/weakly-supervised-3d-pose-generator/eval_functions.py official repository unverified MIT (permissive) · 9d3cc432f01a5229 · report
fc_layer_linear chaneyddtt/weakly-supervised-3d-pose-generator/layers.py official repository unverified MIT (permissive) · 504c9c2b80f5cd48 · report
gp_loss chaneyddtt/weakly-supervised-3d-pose-generator/mmd.py official repository unverified MIT (permissive) · 9393f0343b979f8b · report
mmd2 chaneyddtt/weakly-supervised-3d-pose-generator/mmd.py official repository unverified MIT (permissive) · bf5921658e55ade2 · report
procrustes chaneyddtt/weakly-supervised-3d-pose-generator/eval_functions.py official repository unverified MIT (permissive) · 629ba7666349f226 · report
safer_norm chaneyddtt/weakly-supervised-3d-pose-generator/mmd.py official repository unverified MIT (permissive) · a672706a6b3e25ae · report
weight_variable chaneyddtt/weakly-supervised-3d-pose-generator/layers.py official repository unverified MIT (permissive) · 43d3063aefdee74a · report
weighted_pose_2d_loss chaneyddtt/weakly-supervised-3d-pose-generator/ops.py official repository unverified MIT (permissive) · 81e49b933db41b17 · report
weighted_pose_2d_loss_mj chaneyddtt/weakly-supervised-3d-pose-generator/ops.py official repository unverified MIT (permissive) · 44d4829fafb6dc9f · report
weightedsample chaneyddtt/weakly-supervised-3d-pose-generator/ops.py official repository unverified MIT (permissive) · 545bd5e99804c3ee · report

Tasks

3D Human Pose EstimationMulti-Hypotheses 3D Human Pose EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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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