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arXiv · 2604.10551

NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results

Xin Li·Jiachao Gong·Xijun Wang·Shiyao Xiong·Bingchen Li·Suhang Yao·Chao Zhou·Zhibo Chen·Radu Timofte·Yuxiang Chen·Shibo Yin·Yilian Zhong·Yushun Fang·Xilei Zhu·Yahui Wang·Chen Lu·Meisong Zheng·Xiaoxu Chen·Jing Yang·Zhaokun Hu·Jiahui Liu·Ying Chen·Haoran Bai·Sibin Deng·Shengxi Li·Mai Xu·Junyang Chen·Hao Chen·Xinzhe Zhu·Fengkai Zhang·Long Sun·Yixing Yang·Xindong Zhang·Jiangxin Dong·Jinshan Pan·Jiyuan Zhang·Shuai Liu·Yibin Huang·Xiaotao Wang·Lei Lei·Zhirui Liu·Shinan Chen·Shang-Quan Sun·Wenqi Ren·Jingyi Xu·Zihong Chen·Zhuoya Zou·Xiuhao Qiu·Jingyu Ma·Huiyuan Fu·Kun Liu·Huadong Ma·Dehao Feng·Zhijie Ma·Boqi Zhang·Jiawei Shi·Hao Kang·Yixin Yang·Yeying Jin·Xu Cheng·Yuxuan Jiang·Chengxi Zeng·Tianhao Peng·Fan Zhang·David Bull·Yanan Xing·Jiachen Tu·Guoyi Xu·Yaoxin Jiang·Jiajia Liu·Yaokun Shi·Wei Zhou·Linfeng Li·Hang Song·Qi Xu·Kun Yuan·Yizhen Shao·Yulin Ren
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Abstract

This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-UGC) video restoration benchmark, termed KwaiVIR, which is contributed by USTC and Kuaishou Technology. It contains both synthetically distorted videos and real-world short-form UGC videos in the wild. For this edition, the released data include 200 synthetic training videos, 48 wild training videos, 11 validation videos, and 20 testing videos. The primary goal of this challenge is to establish a strong and practical benchmark for restoring short-form UGC videos under complex real-world degradations, especially in the emerging paradigm of generative-model-based S-UGC video restoration. This challenge has two tracks: (i) the primary track is a subjective track, where the evaluation is based on a user study; (ii) the second track is an objective track. These two tracks enable a comprehensive assessment of restoration quality. In total, 95 teams have registered for this competition. And 12 teams submitted valid final solutions and fact sheets for the testing phase. The submitted methods achieved strong performance on the KwaiVIR benchmark, demonstrating encouraging progress in short-form UGC video restoration in the wild.

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Xin Li, Jiachao Gong, Xijun Wang, Shiyao Xiong, Bingchen Li, Suhang Yao, Chao Zhou, Zhibo Chen, Radu Timofte, Yuxiang Chen, Shibo Yin, Yilian Zhong, Yushun Fang, Xilei Zhu, Yahui Wang, Chen Lu, Meisong Zheng, Xiaoxu Chen, Jing Yang, Zhaokun Hu, Jiahui Liu, Ying Chen, Haoran Bai, Sibin Deng, Shengxi Li, Mai Xu, Junyang Chen, Hao Chen, Xinzhe Zhu, Fengkai Zhang, Long Sun, Yixing Yang, Xindong Zhang, Jiangxin Dong, Jinshan Pan, Jiyuan Zhang, Shuai Liu, Yibin Huang, Xiaotao Wang, Lei Lei, Zhirui Liu, Shinan Chen, Shang-Quan Sun, Wenqi Ren, Jingyi Xu, Zihong Chen, Zhuoya Zou, Xiuhao Qiu, Jingyu Ma, Huiyuan Fu, Kun Liu, Huadong Ma, Dehao Feng, Zhijie Ma, Boqi Zhang, Jiawei Shi, Hao Kang, Yixin Yang, Yeying Jin, Xu Cheng, Yuxuan Jiang, Chengxi Zeng, Tianhao Peng, Fan Zhang, David Bull, Yanan Xing, Jiachen Tu, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Yaokun Shi, Wei Zhou, Linfeng Li, Hang Song, Qi Xu, Kun Yuan, Yizhen Shao, Yulin Ren. 2026-04-12. NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results. https://arxiv.org/abs/2604.10551

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