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