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.
@article{arxiv.2005.05005,
title = {HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment},
author = {Lingbo Yang and Chang Liu and Pan Wang and Shanshe Wang and Peiran Ren and Siwei Ma and Wen Gao},
journal= {arXiv preprint arXiv:2005.05005},
year = {2021}
}