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Facial semantic guidance (including facial landmarks, facial heatmaps, andfacial parsing maps) and facial generative adversarial networks (GAN) priorhave been widely used in blind face restoration (BFR) in recent years. Althoughexisting BFR methods have achieved good performance in ordinary cases, thesesolutions have limited resilience when applied to face images with seriousdegradation and pose-varied (e.g., looking right, looking left, laughing, etc.)in real-world scenarios. In this work, we propose a well-designed blind facerestoration network with generative facial prior. The proposed network ismainly comprised of an asymmetric codec and a StyleGAN2 prior network. In theasymmetric codec, we adopt a mixed multi-path residual block (MMRB) togradually extract weak texture features of input images, which can betterpreserve the original facial features and avoid excessive fantasy. The MMRB canalso be plug-and-play in other networks. Furthermore, thanks to the affluentand diverse facial priors of the StyleGAN2 model, we adopt it as the primarygenerator network in our proposed method and specially design a novelself-supervised training strategy to fit the distribution closer to the targetand flexibly restore natural and realistic facial details. Extensiveexperiments on synthetic and real-world datasets demonstrate that our modelperforms superior to the prior art for face restoration and facesuper-resolution tasks.

Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior