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Papers › Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network

Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network

11 Apr 2019CVPR 2019 6arXiv:1904.05547archive 2025-07-28

Chen Li, Gim Hee Lee

3D human pose estimation from a monocular image or 2D joints is an ill-posed problem because of depth ambiguity and occluded joints. We argue that 3D human pose estimation from a monocular input is an inverse problem where multiple feasible solutions can exist. In this paper, we propose a novel approach to generate multiple feasible hypotheses of the 3D pose from 2D joints.In contrast to existing deep learning approaches which minimize a mean square error based on an unimodal Gaussian distribution, our method is able to generate multiple feasible hypotheses of 3D pose based on a multimodal mixture density networks. Our experiments show that the 3D poses estimated by our approach from an input of 2D joints are consistent in 2D reprojections, which supports our argument that multiple solutions exist for the 2D-to-3D inverse problem. Furthermore, we show state-of-the-art performance on the Human3.6M dataset in both best hypothesis and multi-view settings, and we demonstrate the generalization capacity of our model by testing on the MPII and MPI-INF-3DHP datasets. Our code is available at the project website.

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chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network officialmentioned in papermentioned on GitHubtfMIT report
vnmr/JointVideoPose3D mentioned on GitHubpytorchnot reachable when probed 2026-09-18 — repositories for recent papers often appear after camera-ready report

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get_action_subset chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/predict_3dpose_mdm.py official repository ran · our draft was wrong MIT (permissive) · f5eee6f4a7a2d3ce · report
Dirichlet_loss chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/mix_den_model.py official repository unverified MIT (permissive) · de6eedce004eb352 · report
camera_to_world_frame chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/cameras.py official repository unverified MIT (permissive) · 6ce042354cb0a857 · report
compute_similarity_transform chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/procrustes.py official repository unverified MIT (permissive) · d15a574f20fa5bea · report
kaiming chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/mix_den_model.py official repository unverified MIT (permissive) · a8756021030e5968 · report
load_data chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/data_utils.py official repository unverified MIT (permissive) · eba040e815a85e27 · report
load_stacked_hourglass chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/data_utils.py official repository unverified MIT (permissive) · 32d5f4c81b3b566f · report
mean_log_Gaussian_like chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/mix_den_model.py official repository unverified MIT (permissive) · a866706041e55d76 · report
normalization_stats chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/data_utils.py official repository unverified MIT (permissive) · 9088d315ea27a453 · report
project_point_radial chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/cameras.py official repository unverified MIT (permissive) · b4846342f739f10e · report
world_to_camera_frame chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network/src/cameras.py official repository unverified MIT (permissive) · c30df769883845b6 · report

Tasks

3D Human Pose EstimationMonocular 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 Human3.6M MDN (Multi-View) Average MPJPE (mm) 49.6 #61 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M MDN (Multi-View) Multi-View or Monocular Multi-View #61 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M MDN (Multi-View) Using 2D ground-truth joints No #61 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M MDN Average MPJPE (mm) 52.7 #77 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M MDN Multi-View or Monocular Monocular #77 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M MDN PA-MPJPE 42.6 #77 of 88 Archive leaderboard report
3D Human Pose Estimation Human3.6M MDN Using 2D ground-truth joints No #77 of 88 Archive leaderboard report
3D Human Pose Estimation MPI-INF-3DHP MDM PCK 67.9 #107 of 108 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M Multimodal Mixture Density Networks Average MPJPE (mm) 52.7 #27 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M Multimodal Mixture Density Networks Frames Needed 1 #27 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M Multimodal Mixture Density Networks Need Ground Truth 2D Pose No #27 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M Multimodal Mixture Density Networks Use Video Sequence No #27 of 52 Archive leaderboard report
Multi-Hypotheses 3D Human Pose Estimation AH36M SMPL-MDN (by 3D Multi-bodies) Best-Hypothesis MPJPE (n = 25) 91.5 #5 of 10 Archive leaderboard report
Multi-Hypotheses 3D Human Pose Estimation AH36M SMPL-MDN (by 3D Multi-bodies) Best-Hypothesis PMPJPE (n = 25) 69.5 #5 of 10 Archive leaderboard report
Multi-Hypotheses 3D Human Pose Estimation AH36M SMPL-MDN (by 3D Multi-bodies) H36M PMPJPE (n = 1) 44.8 #5 of 10 Archive leaderboard report
Multi-Hypotheses 3D Human Pose Estimation AH36M SMPL-MDN (by 3D Multi-bodies) H36M PMPJPE (n = 25) 42.7 #5 of 10 Archive leaderboard report
Multi-Hypotheses 3D Human Pose Estimation AH36M SMPL-MDN (by 3D Multi-bodies) Most-Likely Hypothesis PMPJPE (n = 1) 74.7 #5 of 10 Archive leaderboard report
Multi-Hypotheses 3D Human Pose Estimation Human3.6M MDN Average MPJPE (mm) 52.7 #9 of 12 Archive leaderboard report
Multi-Hypotheses 3D Human Pose Estimation Human3.6M MDN Average PMPJPE (mm) 42.6 #9 of 12 Archive leaderboard report

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