RestorerID: Towards Tuning-Free Face Restoration with ID Preservation

• URL: http://arxiv.org/abs/2411.14125v1
• Date: Thu, 21 Nov 2024 13:50:25 GMT
• Title: RestorerID: Towards Tuning-Free Face Restoration with ID Preservation
• Authors: Jiacheng Ying, Mushui Liu, Zhe Wu, Runming Zhang, Zhu Yu, Siming Fu, Si-Yuan Cao, Chao Wu, Yunlong Yu, Hui-Liang Shen,
• Abstract summary: We propose a tuning-free method named RestorerID that incorporates ID preservation during face restoration. To achieve this, we propose a unified framework to combine the ID injection with the base blind face restoration model. Experimental results on the Celeb-Ref dataset and real-world scenarios demonstrate that RestorerID effectively delivers high-quality face restoration with ID preservation.
• Score: 18.022455458259305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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