Unify3D: An Augmented Holistic End-to-end Monocular 3D Human Reconstruction via Anatomy Shaping and Twins Negotiating
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908408431509504 |
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| author | Yao, Nanjie Zhang, Gangjian Shen, Wenhao Shu, Jian Wang, Hao |
| author_facet | Yao, Nanjie Zhang, Gangjian Shen, Wenhao Shu, Jian Wang, Hao |
| contents | Monocular 3D clothed human reconstruction aims to create a complete 3D avatar from a single image. To tackle the human geometry lacking in one RGB image, current methods typically resort to a preceding model for an explicit geometric representation. For the reconstruction itself, focus is on modeling both it and the input image. This routine is constrained by the preceding model, and overlooks the integrity of the reconstruction task. To address this, this paper introduces a novel paradigm that treats human reconstruction as a holistic process, utilizing an end-to-end network for direct prediction from 2D image to 3D avatar, eliminating any explicit intermediate geometry display. Based on this, we further propose a novel reconstruction framework consisting of two core components: the Anatomy Shaping Extraction module, which captures implicit shape features taking into account the specialty of human anatomy, and the Twins Negotiating Reconstruction U-Net, which enhances reconstruction through feature interaction between two U-Nets of different modalities. Moreover, we propose a Comic Data Augmentation strategy and construct 15k+ 3D human scans to bolster model performance in more complex case input. Extensive experiments on two test sets and many in-the-wild cases show the superiority of our method over SOTA methods. Our demos can be found in : https://e2e3dgsrecon.github.io/e2e3dgsrecon/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unify3D: An Augmented Holistic End-to-end Monocular 3D Human Reconstruction via Anatomy Shaping and Twins Negotiating Yao, Nanjie Zhang, Gangjian Shen, Wenhao Shu, Jian Wang, Hao Computer Vision and Pattern Recognition Monocular 3D clothed human reconstruction aims to create a complete 3D avatar from a single image. To tackle the human geometry lacking in one RGB image, current methods typically resort to a preceding model for an explicit geometric representation. For the reconstruction itself, focus is on modeling both it and the input image. This routine is constrained by the preceding model, and overlooks the integrity of the reconstruction task. To address this, this paper introduces a novel paradigm that treats human reconstruction as a holistic process, utilizing an end-to-end network for direct prediction from 2D image to 3D avatar, eliminating any explicit intermediate geometry display. Based on this, we further propose a novel reconstruction framework consisting of two core components: the Anatomy Shaping Extraction module, which captures implicit shape features taking into account the specialty of human anatomy, and the Twins Negotiating Reconstruction U-Net, which enhances reconstruction through feature interaction between two U-Nets of different modalities. Moreover, we propose a Comic Data Augmentation strategy and construct 15k+ 3D human scans to bolster model performance in more complex case input. Extensive experiments on two test sets and many in-the-wild cases show the superiority of our method over SOTA methods. Our demos can be found in : https://e2e3dgsrecon.github.io/e2e3dgsrecon/. |
| title | Unify3D: An Augmented Holistic End-to-end Monocular 3D Human Reconstruction via Anatomy Shaping and Twins Negotiating |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.18215 |