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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.13984 |
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| _version_ | 1866915238784270336 |
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| author | Wang, Chengrui Liu, Pengfei Zhou, Min Zeng, Ming Li, Xubin Ge, Tiezheng zheng, Bo |
| author_facet | Wang, Chengrui Liu, Pengfei Zhou, Min Zeng, Ming Li, Xubin Ge, Tiezheng zheng, Bo |
| contents | Although diffusion models can generate high-quality human images, their applications are limited by the instability in generating hands with correct structures. In this paper, we introduce RHanDS, a conditional diffusion-based framework designed to refine malformed hands by utilizing decoupled structure and style guidance. The hand mesh reconstructed from the malformed hand offers structure guidance for correcting the structure of the hand, while the malformed hand itself provides style guidance for preserving the style of the hand. To alleviate the mutual interference between style and structure guidance, we introduce a two-stage training strategy and build a series of multi-style hand datasets. In the first stage, we use paired hand images for training to ensure stylistic consistency in hand refining. In the second stage, various hand images generated based on human meshes are used for training, enabling the model to gain control over the hand structure. Experimental results demonstrate that RHanDS can effectively refine hand structure while preserving consistency in hand style. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13984 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RHanDS: Refining Malformed Hands for Generated Images with Decoupled Structure and Style Guidance Wang, Chengrui Liu, Pengfei Zhou, Min Zeng, Ming Li, Xubin Ge, Tiezheng zheng, Bo Computer Vision and Pattern Recognition Although diffusion models can generate high-quality human images, their applications are limited by the instability in generating hands with correct structures. In this paper, we introduce RHanDS, a conditional diffusion-based framework designed to refine malformed hands by utilizing decoupled structure and style guidance. The hand mesh reconstructed from the malformed hand offers structure guidance for correcting the structure of the hand, while the malformed hand itself provides style guidance for preserving the style of the hand. To alleviate the mutual interference between style and structure guidance, we introduce a two-stage training strategy and build a series of multi-style hand datasets. In the first stage, we use paired hand images for training to ensure stylistic consistency in hand refining. In the second stage, various hand images generated based on human meshes are used for training, enabling the model to gain control over the hand structure. Experimental results demonstrate that RHanDS can effectively refine hand structure while preserving consistency in hand style. |
| title | RHanDS: Refining Malformed Hands for Generated Images with Decoupled Structure and Style Guidance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.13984 |