{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-real-world-blind-face-restoration","title":"Towards Real-World Blind Face Restoration with Generative Facial Prior","arxiv_id":"2101.04061","date":"2021-01-11","proceeding":"CVPR 2021 1","authors":["Xintao Wang","Yu Li","Honglun Zhang","Ying Shan"],"abstract":"Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.","url_abs":"https://arxiv.org/abs/2101.04061v2","url_pdf":"https://arxiv.org/pdf/2101.04061v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-real-world-blind-face-restoration","repo_url":"https://github.com/TencentARC/GFPGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"blind-face-restoration","task_name":"Blind Face Restoration"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[{"method_slug":"adaptive-instance-normalization","method_name":"Adaptive Instance Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"gfp-gan","method_name":"GFP-GAN"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"r1-regularization","method_name":"R1 Regularization"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"spatial-feature-transform","method_name":"Spatial Feature Transform"},{"method_slug":"stylegan","method_name":"StyleGAN"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/blind-face-restoration-on-celeba-test","task":"Blind Face Restoration","dataset":"CelebA-Test","model":"GFP-GAN","rank_in_archive_order":2,"of":15,"metrics":{"Deg.":"34.60","FID":"42.62","LPIPS":"36.46","NIQE":"4.077","PSNR":"25.08","SSIM":"0.6777"},"uses_additional_data":false},{"leaderboard":"/sota/video-super-resolution-on-msu-vsr-benchmark","task":"Video Super-Resolution","dataset":"MSU Video Super Resolution Benchmark: Detail Restoration","model":"GFPGAN","rank_in_archive_order":30,"of":32,"metrics":{"1 - LPIPS":"0.793","ERQAv1.0":"0.538","FPS":"1.562","PSNR":"24.195","QRCRv1.0":"0","SSIM":"0.745","Subjective score":"2.686"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2101.04061","atlas_url":"https://app.syntology.ai/?focus=2101.04061","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}