High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912971510251520 |
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| author | He, Dailan Wang, Xiahong Wang, Shulun Song, Guanglu Ma, Bingqi Shao, Hao Liu, Yu Li, Hongsheng |
| author_facet | He, Dailan Wang, Xiahong Wang, Shulun Song, Guanglu Ma, Bingqi Shao, Hao Liu, Yu Li, Hongsheng |
| contents | Face swapping aims to seamlessly transfer a source facial identity onto a target while preserving target attributes such as pose and expression. Diffusion models, known for their superior generative capabilities, have recently shown promise in advancing face-swapping quality. This paper addresses two key challenges in diffusion-based face swapping: the prioritized preservation of identity over target attributes and the inherent conflict between identity and attribute conditioning. To tackle these issues, we introduce an identity-constrained attribute-tuning framework for face swapping that first ensures identity preservation and then fine-tunes for attribute alignment, achieved through a decoupled condition injection. We further enhance fidelity by incorporating identity and adversarial losses in a post-training refinement stage. Our proposed identity-constrained diffusion-based face-swapping model outperforms existing methods in both qualitative and quantitative evaluations, demonstrating superior identity similarity and attribute consistency, achieving a new state-of-the-art performance in high-fidelity face swapping. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning He, Dailan Wang, Xiahong Wang, Shulun Song, Guanglu Ma, Bingqi Shao, Hao Liu, Yu Li, Hongsheng Computer Vision and Pattern Recognition Face swapping aims to seamlessly transfer a source facial identity onto a target while preserving target attributes such as pose and expression. Diffusion models, known for their superior generative capabilities, have recently shown promise in advancing face-swapping quality. This paper addresses two key challenges in diffusion-based face swapping: the prioritized preservation of identity over target attributes and the inherent conflict between identity and attribute conditioning. To tackle these issues, we introduce an identity-constrained attribute-tuning framework for face swapping that first ensures identity preservation and then fine-tunes for attribute alignment, achieved through a decoupled condition injection. We further enhance fidelity by incorporating identity and adversarial losses in a post-training refinement stage. Our proposed identity-constrained diffusion-based face-swapping model outperforms existing methods in both qualitative and quantitative evaluations, demonstrating superior identity similarity and attribute consistency, achieving a new state-of-the-art performance in high-fidelity face swapping. |
| title | High-Fidelity Diffusion Face Swapping with ID-Constrained Facial Conditioning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.22179 |