  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. Specifically, an unsupervised degradation representation
learning (UDRL) strategy is first developed to extract degradation
representations (DR) of the input degraded images. Then, a degradation-aware
feature interpolation (DAFI) module is proposed to dynamically fuse the two
types of informative features (\textit{i.e.}, features from input images and
features from GAN Prior) with flexible adaption to various degradations based
on DR. Ablation studies reveal the working mechanism of DAFI and its potential
for editable FR. Extensive experiments demonstrate that our Panini-Net achieves
state-of-the-art performance for multi-degradation face restoration and face
super-resolution. The source code is available at
https://github.com/jianzhangcs/panini.
