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