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Heuristic Weakly Supervised 3D Human Pose Estimation

23 May 2021arXiv:2105.10996archive 2025-07-28

Shuangjun Liu, Michael Wan, Sarah Ostadabbas

Monocular 3D human pose estimation from RGB images has attracted significant attention in recent years. However, recent models depend on supervised training with 3D pose ground truth data or known pose priors for their target domains. 3D pose data is typically collected with motion capture devices, severely limiting their applicability. In this paper, we present a heuristic weakly supervised 3D human pose (HW-HuP) solution to estimate 3D poses in when no ground truth 3D pose data is available. HW-HuP learns partial pose priors from 3D human pose datasets and uses easy-to-access observations from the target domain to estimate 3D human pose and shape in an optimization and regression cycle. We employ depth data for weak supervision during training, but not inference. We show that HW-HuP meaningfully improves upon state-of-the-art models in two practical settings where 3D pose data can hardly be obtained: human poses in bed, and infant poses in the wild. Furthermore, we show that HW-HuP retains comparable performance to cutting-edge models on public benchmarks, even when such models train on 3D pose data.

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ostadabbas/hw-hup officialmentioned in papermentioned on GitHubpytorch report
ostadabbas/infant-postural-symmetry mentioned on GitHub report

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Tasks

3D Human Pose Estimation3D Pose EstimationMonocular 3D Human Pose EstimationPose EstimationWeakly-supervised 3D Human Pose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation 3DPW HW-HuP PA-MPJPE 66.1 #101 of 119 Archive leaderboard report
Weakly-supervised 3D Human Pose Estimation Human3.6M HW-HuP Average MPJPE (mm) 104.1 #26 of 33 Archive leaderboard report
Weakly-supervised 3D Human Pose Estimation Human3.6M HW-HuP PA-MPJPE 50.4 #26 of 33 Archive leaderboard report

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