Reference-Guided Identity Preserving Face Restoration
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910971346288640 |
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| author | Zhou, Mo Ye, Keren Shah, Viraj Mei, Kangfu Delbracio, Mauricio Milanfar, Peyman Patel, Vishal M. Talebi, Hossein |
| author_facet | Zhou, Mo Ye, Keren Shah, Viraj Mei, Kangfu Delbracio, Mauricio Milanfar, Peyman Patel, Vishal M. Talebi, Hossein |
| contents | Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods often fail to fully exploit their potential. This paper introduces a novel approach that maximizes reference face utility for improved face restoration and identity preservation. Our method makes three key contributions: 1) Composite Context, a comprehensive representation that fuses multi-level (high- and low-level) information from the reference face, offering richer guidance than prior singular representations. 2) Hard Example Identity Loss, a novel loss function that leverages the reference face to address the identity learning inefficiencies found in the existing identity loss. 3) A training-free method to adapt the model to multi-reference inputs during inference. The proposed method demonstrably restores high-quality faces and achieves state-of-the-art identity preserving restoration on benchmarks such as FFHQ-Ref and CelebA-Ref-Test, consistently outperforming previous work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reference-Guided Identity Preserving Face Restoration Zhou, Mo Ye, Keren Shah, Viraj Mei, Kangfu Delbracio, Mauricio Milanfar, Peyman Patel, Vishal M. Talebi, Hossein Computer Vision and Pattern Recognition Multimedia Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods often fail to fully exploit their potential. This paper introduces a novel approach that maximizes reference face utility for improved face restoration and identity preservation. Our method makes three key contributions: 1) Composite Context, a comprehensive representation that fuses multi-level (high- and low-level) information from the reference face, offering richer guidance than prior singular representations. 2) Hard Example Identity Loss, a novel loss function that leverages the reference face to address the identity learning inefficiencies found in the existing identity loss. 3) A training-free method to adapt the model to multi-reference inputs during inference. The proposed method demonstrably restores high-quality faces and achieves state-of-the-art identity preserving restoration on benchmarks such as FFHQ-Ref and CelebA-Ref-Test, consistently outperforming previous work. |
| title | Reference-Guided Identity Preserving Face Restoration |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2505.21905 |