Towards Real-World Blind Face Restoration with Generative Facial Prior

CVPR 2021  ·  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. read more

PDF Abstract CVPR 2021 PDF CVPR 2021 Abstract

Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Blind Face Restoration CelebA-Test GFP-GAN LPIPS 36.46 # 1
FID 42.62 # 1
NIQE 4.077 # 1
Deg. 34.60 # 1
PSNR 25.08 # 2
SSIM 0.6777 # 2

Methods


No methods listed for this paper. Add relevant methods here