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.

本文提出了一种利用预训练人脸 GAN 中不同先验知识的 GFP-GAN 盲目人脸修复方法，其通过创新的通道分离空间特征变换层将生成式人脸先验纳入人脸修复过程，达到真实性和保真度之间的良好平衡。与 GAN 反演法相比，GFP-GAN 能够通过单个前向传递联合修复面部细节并增强颜色。大量实验表明，我们的方法在合成和实际数据集上比现有技术具有优越的性能。