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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2504.14600 |
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| _version_ | 1866908328670527488 |
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| author | Chen, Zheng Wang, Jingkai Liu, Kai Gong, Jue Sun, Lei Wu, Zongwei Timofte, Radu Zhang, Yulun Zhang, Jianxing Wu, Jinlong Wang, Jun Xie, Zheng Jeon, Hakjae Han, Suejin Chun, Hyung-Ju Park, Hyunhee Yin, Zhicun Chen, Junjie Liu, Ming Li, Xiaoming Zhou, Chao Zuo, Wangmeng Zhang, Weixia Li, Dingquan Ma, Kede Zhang, Yun Zheng, Zhuofan Liu, Yuyue Tang, Shizhen Zhang, Zihao Ning, Yi Jiang, Hao An, Wenjie Yu, Kangmeng Wang, Chenyang Jiang, Kui Liu, Xianming Jiang, Junjun Zhang, Yingfu He, Gang Wang, Siqi Xu, Kepeng Liu, Zhenyang Zhou, Changxin Shen, Shanlan Duan, Yubo Chen, Yiang Guo, Jin Yang, Mengru Lee, Jen-Wei Lee, Chia-Ming Hsu, Chih-Chung Peng, Hu He, Chunming |
| author_facet | Chen, Zheng Wang, Jingkai Liu, Kai Gong, Jue Sun, Lei Wu, Zongwei Timofte, Radu Zhang, Yulun Zhang, Jianxing Wu, Jinlong Wang, Jun Xie, Zheng Jeon, Hakjae Han, Suejin Chun, Hyung-Ju Park, Hyunhee Yin, Zhicun Chen, Junjie Liu, Ming Li, Xiaoming Zhou, Chao Zuo, Wangmeng Zhang, Weixia Li, Dingquan Ma, Kede Zhang, Yun Zheng, Zhuofan Liu, Yuyue Tang, Shizhen Zhang, Zihao Ning, Yi Jiang, Hao An, Wenjie Yu, Kangmeng Wang, Chenyang Jiang, Kui Liu, Xianming Jiang, Junjun Zhang, Yingfu He, Gang Wang, Siqi Xu, Kepeng Liu, Zhenyang Zhou, Changxin Shen, Shanlan Duan, Yubo Chen, Yiang Guo, Jin Yang, Mengru Lee, Jen-Wei Lee, Chia-Ming Hsu, Chih-Chung Peng, Hu He, Chunming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14600 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Chen, Zheng Wang, Jingkai Liu, Kai Gong, Jue Sun, Lei Wu, Zongwei Timofte, Radu Zhang, Yulun Zhang, Jianxing Wu, Jinlong Wang, Jun Xie, Zheng Jeon, Hakjae Han, Suejin Chun, Hyung-Ju Park, Hyunhee Yin, Zhicun Chen, Junjie Liu, Ming Li, Xiaoming Zhou, Chao Zuo, Wangmeng Zhang, Weixia Li, Dingquan Ma, Kede Zhang, Yun Zheng, Zhuofan Liu, Yuyue Tang, Shizhen Zhang, Zihao Ning, Yi Jiang, Hao An, Wenjie Yu, Kangmeng Wang, Chenyang Jiang, Kui Liu, Xianming Jiang, Junjun Zhang, Yingfu He, Gang Wang, Siqi Xu, Kepeng Liu, Zhenyang Zhou, Changxin Shen, Shanlan Duan, Yubo Chen, Yiang Guo, Jin Yang, Mengru Lee, Jen-Wei Lee, Chia-Ming Hsu, Chih-Chung Peng, Hu He, Chunming Computer Vision and Pattern Recognition 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. |
| title | NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.14600 |