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.

本研究针对现有的面部修复技术的限制，提出了一种名为 Face Renovation（FR）的语义引导生成方法，并采用协同抑制补充（CSR）策略解决这个更具挑战性和实际问题，最终构建了多阶段嵌套 CSR 单元的 HiFaceGAN。实验证明，HiFaceGAN 在包含丰富背景干扰的真实图像中具有出色的修复性能，展示了其在面部图像处理中的多用途、稳健性和泛化能力。