Papers › Towards Real-World Blind Face Restoration with Generative Facial Prior

Towards Real-World Blind Face Restoration with Generative Facial Prior

11 Jan 2021CVPR 2021 1arXiv:2101.04061archive 2025-07-28

Xintao Wang, Yu Li, Honglun Zhang, Ying Shan

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.

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Code

TencentARC/GFPGAN officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Blind Face RestorationVideo Super-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Blind Face Restoration CelebA-Test GFP-GAN Deg. 34.60 #2 of 15 Archive leaderboard report
Blind Face Restoration CelebA-Test GFP-GAN FID 42.62 #2 of 15 Archive leaderboard report
Blind Face Restoration CelebA-Test GFP-GAN LPIPS 36.46 #2 of 15 Archive leaderboard report
Blind Face Restoration CelebA-Test GFP-GAN NIQE 4.077 #2 of 15 Archive leaderboard report
Blind Face Restoration CelebA-Test GFP-GAN PSNR 25.08 #2 of 15 Archive leaderboard report
Blind Face Restoration CelebA-Test GFP-GAN SSIM 0.6777 #2 of 15 Archive leaderboard report
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration GFPGAN 1 - LPIPS 0.793 #30 of 32 Archive leaderboard report
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration GFPGAN ERQAv1.0 0.538 #30 of 32 Archive leaderboard report
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration GFPGAN FPS 1.562 #30 of 32 Archive leaderboard report
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration GFPGAN PSNR 24.195 #30 of 32 Archive leaderboard report
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration GFPGAN QRCRv1.0 0 #30 of 32 Archive leaderboard report
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration GFPGAN SSIM 0.745 #30 of 32 Archive leaderboard report
Video Super-Resolution MSU Video Super Resolution Benchmark: Detail Restoration GFPGAN Subjective score 2.686 #30 of 32 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adaptive Instance NormalizationConcatenated Skip ConnectionConvolutionDense ConnectionsFeedforward NetworkGFP-GANMax PoolingR1 RegularizationReLUSpatial Feature TransformStyleGANU-Net

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