NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
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.
View on arXiv@article{chen2025_2504.14600,
title={ NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results },
author={ Zheng Chen and Jingkai Wang and Kai Liu and Jue Gong and Lei Sun and Zongwei Wu and Radu Timofte and Yulun Zhang and Jianxing Zhang and Jinlong Wu and Jun Wang and Zheng Xie and Hakjae Jeon and Suejin Han and Hyung-Ju Chun and Hyunhee Park and Zhicun Yin and Junjie Chen and Ming Liu and Xiaoming Li and Chao Zhou and Wangmeng Zuo and Weixia Zhang and Dingquan Li and Kede Ma and Yun Zhang and Zhuofan Zheng and Yuyue Liu and Shizhen Tang and Zihao Zhang and Yi Ning and Hao Jiang and Wenjie An and Kangmeng Yu and Chenyang Wang and Kui Jiang and Xianming Liu and Junjun Jiang and Yingfu Zhang and Gang He and Siqi Wang and Kepeng Xu and Zhenyang Liu and Changxin Zhou and Shanlan Shen and Yubo Duan and Yiang Chen and Jin Guo and Mengru Yang and Jen-Wei Lee and Chia-Ming Lee and Chih-Chung Hsu and Hu Peng and Chunming He },
journal={arXiv preprint arXiv:2504.14600},
year={ 2025 }
}