Towards Real-World Blind Face Restoration with Generative Facial Prior

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 spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. 2021: Xintao Wang, Yu Li, Honglun Zhang, Ying Shan Ranked #1 on Blind Face Restoration on CelebA-Test https://arxiv.org/pdf/2101.04061v2.pdf