  Facial semantic guidance (including facial landmarks, facial heatmaps, and
facial parsing maps) and facial generative adversarial networks (GAN) prior
have been widely used in blind face restoration (BFR) in recent years. Although
existing BFR methods have achieved good performance in ordinary cases, these
solutions have limited resilience when applied to face images with serious
degradation and pose-varied (e.g., looking right, looking left, laughing, etc.)
in real-world scenarios. In this work, we propose a well-designed blind face
restoration network with generative facial prior. The proposed network is
mainly comprised of an asymmetric codec and a StyleGAN2 prior network. In the
asymmetric codec, we adopt a mixed multi-path residual block (MMRB) to
gradually extract weak texture features of input images, which can better
preserve the original facial features and avoid excessive fantasy. The MMRB can
also be plug-and-play in other networks. Furthermore, thanks to the affluent
and diverse facial priors of the StyleGAN2 model, we adopt it as the primary
generator network in our proposed method and specially design a novel
self-supervised training strategy to fit the distribution closer to the target
and flexibly restore natural and realistic facial details. Extensive
experiments on synthetic and real-world datasets demonstrate that our model
performs superior to the prior art for face restoration and face
super-resolution tasks.
