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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/2405.12970 |
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| _version_ | 1866911948671549440 |
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| author | Han, Yue Zhu, Junwei He, Keke Chen, Xu Ge, Yanhao Li, Wei Li, Xiangtai Zhang, Jiangning Wang, Chengjie Liu, Yong |
| author_facet | Han, Yue Zhu, Junwei He, Keke Chen, Xu Ge, Yanhao Li, Wei Li, Xiangtai Zhang, Jiangning Wang, Chengjie Liu, Yong |
| contents | Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce Face-Adapter, an efficient and effective adapter designed for high-precision and high-fidelity face editing for pre-trained diffusion models. We observe that both face reenactment/swapping tasks essentially involve combinations of target structure, ID and attribute. We aim to sufficiently decouple the control of these factors to achieve both tasks in one model. Specifically, our method contains: 1) A Spatial Condition Generator that provides precise landmarks and background; 2) A Plug-and-play Identity Encoder that transfers face embeddings to the text space by a transformer decoder. 3) An Attribute Controller that integrates spatial conditions and detailed attributes. Face-Adapter achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned face reenactment/swapping models. Additionally, Face-Adapter seamlessly integrates with various StableDiffusion models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12970 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control Han, Yue Zhu, Junwei He, Keke Chen, Xu Ge, Yanhao Li, Wei Li, Xiangtai Zhang, Jiangning Wang, Chengjie Liu, Yong Computer Vision and Pattern Recognition Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce Face-Adapter, an efficient and effective adapter designed for high-precision and high-fidelity face editing for pre-trained diffusion models. We observe that both face reenactment/swapping tasks essentially involve combinations of target structure, ID and attribute. We aim to sufficiently decouple the control of these factors to achieve both tasks in one model. Specifically, our method contains: 1) A Spatial Condition Generator that provides precise landmarks and background; 2) A Plug-and-play Identity Encoder that transfers face embeddings to the text space by a transformer decoder. 3) An Attribute Controller that integrates spatial conditions and detailed attributes. Face-Adapter achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned face reenactment/swapping models. Additionally, Face-Adapter seamlessly integrates with various StableDiffusion models. |
| title | Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.12970 |