HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

• URL: http://arxiv.org/abs/2005.05005v2
• Date: Sat, 22 May 2021 12:12:36 GMT
• Title: HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
• Authors: Lingbo Yang, Chang Liu, Pan Wang, Shanshe Wang, Peiran Ren, Siwei Ma, Wen Gao
• Abstract summary: "Face Renovation"(FR) is a semantic-guided generation problem. "HiFaceGAN" is a multi-stage framework containing several nested CSR units. experiments on both synthetic and real face images have verified the superior performance of HiFaceGAN.
• Score: 63.333407973913374
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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