English(EN) UT-HMR: Uncertainty-Guided Temporal Modeling for Robust 3D Human Mesh Recovery

新框架通过不确定性建模增强3D人体网格恢复

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了UT-HMR，一种用于从单目图像恢复3D人体网格的新型单阶段框架。该系统通过捕捉跨关节的相关姿势不确定性，解决了长距离、遮挡和运动模糊等挑战。UT-HMR预测姿势上的高斯分布，将其转换为监督的精度权重，而时间早期融合则整合来自相邻帧的证据来解决歧义。该框架利用Vision Transformer Base，在VIP-HockeyPose-v2和WorldPose等专业数据集上取得了最先进的结果。 AI

影响提高了从单目视频进行3D人体姿势估计的准确性，尤其是在挑战性条件下。

排序理由关于3D人体网格恢复新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Zhibo Wang, Sirisha Rambhatla, Yuhao Chen, Maria Koshkina, David Clausi · 2026-10-28 04:00

UT-HMR：用于鲁棒三维人体网格恢复的不确定性引导时序建模

arXiv:2610.36203v1 Announce Type: new 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 …

本文如何被排名

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UT-HMR：用于鲁棒三维人体网格恢复的不确定性引导时序建模

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