Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration

Understand

Emerging high-quality face restoration (FR) methods often utilize pre-trained GAN models (\textit{i.e.}, StyleGAN2) as GAN Prior.

• However, these methods usually struggle to balance realness and fidelity when facing various degradation levels.
• Besides, there is still a noticeable visual quality gap compared with pre-trained GAN models.
• In this paper, we propose a novel GAN Prior based degradation-aware feature interpolation network, dubbed Panini-Net, for FR tasks by explicitly learning the abstract representations to distinguish various degradations.