Papers › GFPose: Learning 3D Human Pose Prior with Gradient Fields
GFPose: Learning 3D Human Pose Prior with Gradient Fields
Hai Ci, Mingdong Wu, Wentao Zhu, Xiaoxuan Ma, Hao Dong, Fangwei Zhong, Yizhou Wang
Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D human pose to match a given task specification. During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework. Despite the simplicity, GFPose demonstrates great potential in several downstream tasks. Our experiments empirically show that 1) as a multi-hypothesis pose estimator, GFPose outperforms existing SOTAs by 20% on Human3.6M dataset. 2) as a single-hypothesis pose estimator, GFPose achieves comparable results to deterministic SOTAs, even with a vanilla backbone. 3) GFPose is able to produce diverse and realistic samples in pose denoising, completion and generation tasks. Project page https://sites.google.com/view/gfpose/
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Hypotheses 3D Human Pose Estimation | Human3.6M | GFPose (HPJ2D-010, S=200) | Average MPJPE (mm) | 35.1 | #2 of 12 | Archive leaderboard | report |
| Multi-Hypotheses 3D Human Pose Estimation | Human3.6M | GFPose (HPJ2D-000, S=200) | Average MPJPE (mm) | 35.6 | #4 of 12 | Archive leaderboard | report |
| Multi-Hypotheses 3D Human Pose Estimation | Human3.6M | GFPose (HPJ2D-000, S=200) | Average PMPJPE (mm) | 30.5 | #4 of 12 | Archive leaderboard | report |
| Multi-Hypotheses 3D Human Pose Estimation | Human3.6M | GFPose (HPJ2D-000, S=200) | Using 2D ground-truth joints | 16.9 | #4 of 12 | Archive leaderboard | report |
| Multi-Hypotheses 3D Human Pose Estimation | MPI-INF-3DHP | GFPose (HPJ2D-000, S=200) | PCK | 86.9 | #1 of 1 | 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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