  Existing face restoration researches typically relies on either the
degradation prior or explicit guidance labels for training, which often results
in limited generalization ability over real-world images with heterogeneous
degradations and rich background contents. In this paper, we investigate the
more challenging and practical "dual-blind" version of the problem by lifting
the requirements on both types of prior, termed as "Face Renovation"(FR).
Specifically, we formulated FR as a semantic-guided generation problem and
tackle it with a collaborative suppression and replenishment (CSR) approach.
This leads to HiFaceGAN, a multi-stage framework containing several nested CSR
units that progressively replenish facial details based on the hierarchical
semantic guidance extracted from the front-end content-adaptive suppression
modules. Extensive experiments on both synthetic and real face images have
verified the superior performance of HiFaceGAN over a wide range of challenging
restoration subtasks, demonstrating its versatility, robustness and
generalization ability towards real-world face processing applications.
