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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.05804 |
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| _version_ | 1866916330660167680 |
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| author | Wang, Yi Ma, Jian Shao, Ruizhi Feng, Qiao Lai, Yu-Kun Liu, Yebin Li, Kun |
| author_facet | Wang, Yi Ma, Jian Shao, Ruizhi Feng, Qiao Lai, Yu-Kun Liu, Yebin Li, Kun |
| contents | The generation of 3D clothed humans has attracted increasing attention in recent years. However, existing work cannot generate layered high-quality 3D humans with consistent body structures. As a result, these methods are unable to arbitrarily and separately change and edit the body and clothing of the human. In this paper, we propose a text-driven layered 3D human generation framework based on a novel physically-decoupled semantic-aware diffusion model. To keep the generated clothing consistent with the target text, we propose a semantic-confidence strategy for clothing that can eliminate the non-clothing content generated by the model. To match the clothing with different body shapes, we propose a SMPL-driven implicit field deformation network that enables the free transfer and reuse of clothing. Besides, we introduce uniform shape priors based on the SMPL model for body and clothing, respectively, which generates more diverse 3D content without being constrained by specific templates. The experimental results demonstrate that the proposed method not only generates 3D humans with consistent body structures but also allows free editing in a layered manner. The source code will be made public. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05804 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Layered 3D Human Generation via Semantic-Aware Diffusion Model Wang, Yi Ma, Jian Shao, Ruizhi Feng, Qiao Lai, Yu-Kun Liu, Yebin Li, Kun Computer Vision and Pattern Recognition The generation of 3D clothed humans has attracted increasing attention in recent years. However, existing work cannot generate layered high-quality 3D humans with consistent body structures. As a result, these methods are unable to arbitrarily and separately change and edit the body and clothing of the human. In this paper, we propose a text-driven layered 3D human generation framework based on a novel physically-decoupled semantic-aware diffusion model. To keep the generated clothing consistent with the target text, we propose a semantic-confidence strategy for clothing that can eliminate the non-clothing content generated by the model. To match the clothing with different body shapes, we propose a SMPL-driven implicit field deformation network that enables the free transfer and reuse of clothing. Besides, we introduce uniform shape priors based on the SMPL model for body and clothing, respectively, which generates more diverse 3D content without being constrained by specific templates. The experimental results demonstrate that the proposed method not only generates 3D humans with consistent body structures but also allows free editing in a layered manner. The source code will be made public. |
| title | Layered 3D Human Generation via Semantic-Aware Diffusion Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.05804 |