This paper provides a review of the NTIRE 2026 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on generating natural and 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. Performance is evaluated using a weighted image quality assessment (IQA) score and employs the AdaFace model as an identity checker. The competition attracted 96 registrants, with 10 teams submitting valid models; ultimately, 9 teams achieved valid scores 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.
@article{arxiv.2604.10532,
title = {The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results},
author = {Jingkai Wang and Jue Gong and Zheng Chen and Kai Liu and Jiatong Li and Yulun Zhang and Radu Timofte and Jiachen Tu and Yaokun Shi and Guoyi Xu and Yaoxin Jiang and Jiajia Liu and Yingsi Chen and Yijiao Liu and Hui Li and Yu Wang and Congchao Zhu and Alexandru-Gabriel Lefterache and Anamaria Radoi and Chuanyue Yan and Tao Lu and Yanduo Zhang and Kanghui Zhao and Jiaming Wang and Yuqi Li and WenBo Xiong and Yifei Chen and Xian Hu and Wei Deng and Daiguo Zhou and Sujith Roy and Claudia Jesuraj and Vikas B and Spoorthi LC and Nikhil Akalwadi and Ramesh Ashok Tabib and Uma Mudenagudi and Yuxuan Jiang and Chengxi Zeng and Tianhao Peng and Fan Zhang and David Bull Wei Zhou and Linfeng Li and Hongyu Huang and Hoyoung Lee and SangYun Oh and ChangYoung Jeong and Axi Niu and Jinyang Zhang and Zhenguo Wu and Senyan Qing and Jinqiu Sun and Yanning Zhang},
journal= {arXiv preprint arXiv:2604.10532},
year = {2026}
}