Abstract:

In order to restore accurate and realistic details, blind face restoration often uses facial priors, such as a reference prior or a facial geometry prior. The applicability to real-world situations is, however, constrained by the inaccessibility of high quality references and the inability of very low-quality inputs to provide accurate geometric prior. In this paper, we present a GFP-GAN for blind face restoration that takes advantage of rich and varied priors included in a pre-trained face GAN. By the use of spatial feature transform layers, this Generative Facial Prior (GFP) is incorporated into the face restoration process, enabling our method to successfully strike a compromise between realism and fidelity. Whereas GAN inversion methods require image-specific tweaking at inference, our GFP-GAN could simultaneously restore facial details and enhance colours with just a single forward pass because of the powerful generative facial prior and delicate designs. Many tests demonstrate that, on both synthetic and real- world datasets, our technique outperforms earlier art

Description:

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