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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.14125 |
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| _version_ | 1866929599733039104 |
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| author | Ying, Jiacheng Liu, Mushui Wu, Zhe Zhang, Runming Yu, Zhu Fu, Siming Cao, Si-Yuan Wu, Chao Yu, Yunlong Shen, Hui-Liang |
| author_facet | Ying, Jiacheng Liu, Mushui Wu, Zhe Zhang, Runming Yu, Zhu Fu, Siming Cao, Si-Yuan Wu, Chao Yu, Yunlong Shen, Hui-Liang |
| contents | Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradation is heavy. Current reference-guided face restoration approaches either require face alignment or personalized test-tuning, which are unfaithful or time-consuming. In this paper, we propose a tuning-free method named RestorerID that incorporates ID preservation during face restoration. RestorerID is a diffusion model-based method that restores low-quality images with varying levels of degradation by using a single reference image. To achieve this, we propose a unified framework to combine the ID injection with the base blind face restoration model. In addition, we design a novel Face ID Rebalancing Adapter (FIR-Adapter) to tackle the problems of content unconsistency and contours misalignment that are caused by information conflicts between the low-quality input and reference image. Furthermore, by employing an Adaptive ID-Scale Adjusting strategy, RestorerID can produce superior restored images across various levels of degradation. Experimental results on the Celeb-Ref dataset and real-world scenarios demonstrate that RestorerID effectively delivers high-quality face restoration with ID preservation, achieving a superior performance compared to the test-tuning approaches and other reference-guided ones. The code of RestorerID is available at \url{https://github.com/YingJiacheng/RestorerID}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14125 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RestorerID: Towards Tuning-Free Face Restoration with ID Preservation Ying, Jiacheng Liu, Mushui Wu, Zhe Zhang, Runming Yu, Zhu Fu, Siming Cao, Si-Yuan Wu, Chao Yu, Yunlong Shen, Hui-Liang Computer Vision and Pattern Recognition Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradation is heavy. Current reference-guided face restoration approaches either require face alignment or personalized test-tuning, which are unfaithful or time-consuming. In this paper, we propose a tuning-free method named RestorerID that incorporates ID preservation during face restoration. RestorerID is a diffusion model-based method that restores low-quality images with varying levels of degradation by using a single reference image. To achieve this, we propose a unified framework to combine the ID injection with the base blind face restoration model. In addition, we design a novel Face ID Rebalancing Adapter (FIR-Adapter) to tackle the problems of content unconsistency and contours misalignment that are caused by information conflicts between the low-quality input and reference image. Furthermore, by employing an Adaptive ID-Scale Adjusting strategy, RestorerID can produce superior restored images across various levels of degradation. Experimental results on the Celeb-Ref dataset and real-world scenarios demonstrate that RestorerID effectively delivers high-quality face restoration with ID preservation, achieving a superior performance compared to the test-tuning approaches and other reference-guided ones. The code of RestorerID is available at \url{https://github.com/YingJiacheng/RestorerID}. |
| title | RestorerID: Towards Tuning-Free Face Restoration with ID Preservation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.14125 |