[Submitted on 26 Oct 2026]
Title:UT-HMR: Uncertainty-Guided Temporal Modeling for Robust 3D Human Mesh Recovery
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Abstract:Monocular 3D human mesh recovery (HMR) remains unreliable under weak observations caused by long camera-to-person distances, occlusion, and motion blur. In challenging scenarios such as sports, these factors make the problem highly ambiguous, as different 3D poses may yield similar 2D projections. This ambiguity is especially severe for one-stage HMR, which recovers all meshes directly from the full image without person crops or external 2D cues. To handle this ambiguity, we present UT-HMR ( Uncertainty-guided Temporal HMR), to our knowledge the first one-stage HMR framework to capture correlated pose uncertainty across joints. Each person query predicts a Gaussian distribution over the full SMPL pose with a compact low-rank-plus-diagonal covariance, which is converted into joint-wise precision weights for heteroscedastic supervision while keeping inference deterministic. Temporal early fusion further injects neighboring-frame evidence before pose decoding, supplying the visual evidence needed to resolve the ambiguity that the uncertainty captures. With a lightweight ViT-B backbone, UT-HMR achieves state-of-the-art results on VIP-HockeyPose-v2 (ice hockey) and WorldPose (soccer), reducing MPJPE, PA-MPJPE, and PVE by up to 4.7%, 3.8%, and 5.6%, and remains competitive on 3DPW against methods with larger backbones and higher input resolutions.
Comments: Accepted at WACV 2027
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.36203 [cs.CV]
(or arXiv:2610.36203v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.36203
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhibo Wang [view email] [v1] Mon, 26 Oct 2026 19:41:07 UTC (27,058 KB)
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