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Towards Real-World Blind Face Restoration with Generative Facial Prior
In this work, we propose GFPGAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real world datasets.
2021: Xintao Wang, Yu Li, Honglun Zhang, Ying Shan
https://arxiv.org/pdf/2101.04061v2.pdf
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By Rob
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In this work, we propose GFPGAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real world datasets.
2021: Xintao Wang, Yu Li, Honglun Zhang, Ying Shan
https://arxiv.org/pdf/2101.04061v2.pdf