MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912989471309824 |
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| author | Song, Chentao Zhang, He Yuan, Haolei Lin, Haozhe Tao, Jianhua Zhang, Hongwen Yu, Tao |
| author_facet | Song, Chentao Zhang, He Yuan, Haolei Lin, Haozhe Tao, Jianhua Zhang, Hongwen Yu, Tao |
| contents | We introduce MetricHMSR, a novel framework for recovering metric human meshes and 3D scenes from a single monocular image. Existing methods struggle to recover metric scale due to monocular scale ambiguity and weak-perspective camera assumptions. Moreover, their fully coupled feature representations make it difficult to disentangle local pose from global translation, often requiring multi-stage pipelines that introduce accumulated errors. To address these challenges, we propose MetricHMR (Metric Human Mesh Recovery), which incorporates a bounding camera ray map representation to provide explicit metric cues for human reconstruction,together with a Human Mixture-of-Experts (HumanMoE) that dynamically routes image features to specialized experts, enabling the disentangled perception of local human pose and global metric position. Leveraging the recovered metric human as a geometric anchor, we further refine monocular metric depth estimation to achieve more accurate 3D alignment between humans and scenes.Comprehensive experiments demonstrate that our method achieves state-of-the-art performance on both human mesh recovery and metric human-scene reconstruction. Project Page: https://Metaverse-AI-Lab-THU.github.io/MetricHMSR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images Song, Chentao Zhang, He Yuan, Haolei Lin, Haozhe Tao, Jianhua Zhang, Hongwen Yu, Tao Computer Vision and Pattern Recognition We introduce MetricHMSR, a novel framework for recovering metric human meshes and 3D scenes from a single monocular image. Existing methods struggle to recover metric scale due to monocular scale ambiguity and weak-perspective camera assumptions. Moreover, their fully coupled feature representations make it difficult to disentangle local pose from global translation, often requiring multi-stage pipelines that introduce accumulated errors. To address these challenges, we propose MetricHMR (Metric Human Mesh Recovery), which incorporates a bounding camera ray map representation to provide explicit metric cues for human reconstruction,together with a Human Mixture-of-Experts (HumanMoE) that dynamically routes image features to specialized experts, enabling the disentangled perception of local human pose and global metric position. Leveraging the recovered metric human as a geometric anchor, we further refine monocular metric depth estimation to achieve more accurate 3D alignment between humans and scenes.Comprehensive experiments demonstrate that our method achieves state-of-the-art performance on both human mesh recovery and metric human-scene reconstruction. Project Page: https://Metaverse-AI-Lab-THU.github.io/MetricHMSR. |
| title | MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.09919 |