Papers › Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery

Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery

25 Nov 2024arXiv:2411.16289archive 2025-07-28

Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn, Bastian Wandt

Monocular 3D human pose and shape estimation is an inherently ill-posed problem due to depth ambiguities, occlusions, and truncations. Recent probabilistic approaches learn a distribution over plausible 3D human meshes by maximizing the likelihood of the ground-truth pose given an image. We show that this objective function alone is not sufficient to best capture the full distributions. Instead, we propose to additionally supervise the learned distributions by minimizing the distance to distributions encoded in heatmaps of a 2D pose detector. Moreover, we reveal that current methods often generate incorrect hypotheses for invisible joints which is not detected by the evaluation protocols. We demonstrate that person segmentation masks can be utilized during training to significantly decrease the number of invalid samples and introduce two metrics to evaluate it. Our normalizing flow-based approach predicts plausible 3D human mesh hypotheses that are consistent with the image evidence while maintaining high diversity for ambiguous body parts. Experiments on 3DPW and EMDB show that we outperform other state-of-the-art probabilistic methods. Code is available for research purposes at https://github.com/twehrbein/humr.

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keypoint_pck_accuracy twehrbein/humr/easy_vitpose/top_down_eval.py official repository ran · fixture could not drive it MIT (permissive) · 8d099da3b789e317 · report
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fliplr_joints twehrbein/humr/easy_vitpose/post_transforms.py official repository unverified MIT (permissive) · 72ea34cd25c47f9e · report
fliplr_regression twehrbein/humr/easy_vitpose/post_transforms.py official repository unverified MIT (permissive) · 89a0b0a407328fc3 · report
infer_dataset_by_path twehrbein/humr/easy_vitpose/util.py official repository unverified MIT (permissive) · a8ef447fad93468f · report
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keypoint_auc twehrbein/humr/easy_vitpose/top_down_eval.py official repository unverified MIT (permissive) · 117462de1e66d200 · report
pose_pck_accuracy twehrbein/humr/easy_vitpose/top_down_eval.py official repository unverified MIT (permissive) · 6d08729cda37113c · report

Tasks

3D human pose and shape estimationDiversityHuman Mesh Recovery

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