{"type":"ScholarlyArticle","title":"Unleashing the Potential of Transformer Flow for Photorealistic Face Restoration","authors":[{"name":"Kepeng Xu","slug":"kepeng-xu","given":"Kepeng","family":"Xu","affiliation":"Xidian University"},{"name":"Li Xu","slug":"li-xu","given":"Li","family":"Xu","affiliation":"Xidian University"},{"name":"Gang He","slug":"gang-he","given":"Gang","family":"He","affiliation":"Xidian University"},{"name":"Wei Chen","slug":"wei-chen","given":"Wei","family":"Chen","affiliation":"Beihang University"},{"name":"Xianyun Wu","slug":"xianyun-wu","given":"Xianyun","family":"Wu","affiliation":"Xidian University"},{"name":"Wenxin Yu","slug":"wenxin-yu","given":"Wenxin","family":"Yu","affiliation":"Southwest University of Science and Technology; Fujiang Laboratory"}],"abstract":"Face restoration is a challenging task due to the need to remove artifacts and restore details. Traditional methods usually use generative model prior to achieve face restoration, but the restored results are still insufficient in terms of realism and details. In this paper, we introduce OmniFace, a novel face restoration framework that leverages Transformer-based diffusion flow. By exploiting the scaling property of Transformer, OmniFace achieves high-resolution restoration with exceptional realism and detail. The framework integrates three key components: (1) a Transformer-driven vector estimation network, (2) a representation aligned ControlNet, and (3) an adaptive training strategy for face restoration. The inherent scaling law of Transformer architectures enables the restoration of high-quality faces at high resolution. The controlnet combined with pre-trained diffusion representation can be easily trained. The adaptive training strategy provides a vector field that is more suitable for face restoration.  Comprehensive experiments demonstrate that OmniFace outperforms existing techniques in terms of restoration quality across multiple benchmark datasets, especially in restoring photographic-level texture details in high-resolution scenes.","doi":"10.24963/ijcai.2025/234","url":"https://ijcai.org/proceedings/2025/234","pdf":"https://ijcai.org/proceedings/2025/0234.pdf","markdown":"https://ijcai.org/proceedings/2025/234.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/234","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":234,"pages":{"first":2098,"last":2106},"keywords":["Computer Vision: CV: Computational photography","Computer Vision: CV: Low-level Vision","Computer Vision: CV: Image and video synthesis and generation"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2018] Yu Chen, Ying Tai, Xiaoming Liu, Chun- hua Shen, and Jian Yang. 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