Papers › Learning 3D Human Pose from Structure and Motion

Learning 3D Human Pose from Structure and Motion

25 Nov 2017ECCV 2018 9arXiv:1711.09250archive 2025-07-28

Rishabh Dabral, Anurag Mundhada, Uday Kusupati, Safeer Afaque, Abhishek Sharma, Arjun Jain

3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.

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Tasks

3D Human Pose EstimationMonocular 3D Human Pose EstimationPose EstimationWeakly-supervised Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation 3DPW TP-Net PA-MPJPE 92.2 #111 of 119 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TP-Net Average MPJPE (mm) 52.1 #26 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TP-Net Frames Needed 20 #26 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TP-Net Need Ground Truth 2D Pose No #26 of 52 Archive leaderboard report
Monocular 3D Human Pose Estimation Human3.6M TP-Net Use Video Sequence Yes #26 of 52 Archive leaderboard report

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