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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results

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arxiv 2504.14600 v1 pith:UELKCLNC submitted 2025-04-20 cs.CV

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results

Zheng Chen , Jingkai Wang , Kai Liu , Jue Gong , Lei Sun , Zongwei Wu , Radu Timofte , Yulun Zhang
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Jianxing Zhang Jinlong Wu Jun Wang Zheng Xie Hakjae Jeon Suejin Han Hyung-Ju Chun Hyunhee Park Zhicun Yin Junjie Chen Ming Liu Xiaoming Li Chao Zhou Wangmeng Zuo Weixia Zhang Dingquan Li Kede Ma Yun Zhang Zhuofan Zheng Yuyue Liu Shizhen Tang Zihao Zhang Yi Ning Hao Jiang Wenjie An Kangmeng Yu Chenyang Wang Kui Jiang Xianming Liu Junjun Jiang Yingfu Zhang Gang He Siqi Wang Kepeng Xu Zhenyang Liu Changxin Zhou Shanlan Shen Yubo Duan Yiang Chen Jin Guo Mengru Yang Jen-Wei Lee Chia-Ming Lee Chih-Chung Hsu Hu Peng Chunming He
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classification cs.CV
keywords challengefacereal-worldrestorationidentityntireperformancequality
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on generating natural, realistic outputs while maintaining identity consistency. Its goal is to advance state-of-the-art solutions for perceptual quality and realism, without imposing constraints on computational resources or training data. The track of the challenge evaluates performance using a weighted image quality assessment (IQA) score and employs the AdaFace model as an identity checker. The competition attracted 141 registrants, with 13 teams submitting valid models, and ultimately, 10 teams achieved a valid score in the final ranking. This collaborative effort advances the performance of real-world face restoration while offering an in-depth overview of the latest trends in the field.

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