ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908325596102656 |
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| author | Zhang, Zetong Kaufmann, Manuel Xue, Lixin Song, Jie Oswald, Martin R. |
| author_facet | Zhang, Zetong Kaufmann, Manuel Xue, Lixin Song, Jie Oswald, Martin R. |
| contents | Creating a photorealistic scene and human reconstruction from a single monocular in-the-wild video figures prominently in the perception of a human-centric 3D world. Recent neural rendering advances have enabled holistic human-scene reconstruction but require pre-calibrated camera and human poses, and days of training time. In this work, we introduce a novel unified framework that simultaneously performs camera tracking, human pose estimation and human-scene reconstruction in an online fashion. 3D Gaussian Splatting is utilized to learn Gaussian primitives for humans and scenes efficiently, and reconstruction-based camera tracking and human pose estimation modules are designed to enable holistic understanding and effective disentanglement of pose and appearance. Specifically, we design a human deformation module to reconstruct the details and enhance generalizability to out-of-distribution poses faithfully. Aiming to learn the spatial correlation between human and scene accurately, we introduce occlusion-aware human silhouette rendering and monocular geometric priors, which further improve reconstruction quality. Experiments on the EMDB and NeuMan datasets demonstrate superior or on-par performance with existing methods in camera tracking, human pose estimation, novel view synthesis and runtime. Our project page is at https://eth-ait.github.io/ODHSR. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_13167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos Zhang, Zetong Kaufmann, Manuel Xue, Lixin Song, Jie Oswald, Martin R. Computer Vision and Pattern Recognition I.4.5 Creating a photorealistic scene and human reconstruction from a single monocular in-the-wild video figures prominently in the perception of a human-centric 3D world. Recent neural rendering advances have enabled holistic human-scene reconstruction but require pre-calibrated camera and human poses, and days of training time. In this work, we introduce a novel unified framework that simultaneously performs camera tracking, human pose estimation and human-scene reconstruction in an online fashion. 3D Gaussian Splatting is utilized to learn Gaussian primitives for humans and scenes efficiently, and reconstruction-based camera tracking and human pose estimation modules are designed to enable holistic understanding and effective disentanglement of pose and appearance. Specifically, we design a human deformation module to reconstruct the details and enhance generalizability to out-of-distribution poses faithfully. Aiming to learn the spatial correlation between human and scene accurately, we introduce occlusion-aware human silhouette rendering and monocular geometric priors, which further improve reconstruction quality. Experiments on the EMDB and NeuMan datasets demonstrate superior or on-par performance with existing methods in camera tracking, human pose estimation, novel view synthesis and runtime. Our project page is at https://eth-ait.github.io/ODHSR. |
| title | ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos |
| topic | Computer Vision and Pattern Recognition I.4.5 |
| url | https://arxiv.org/abs/2504.13167 |