SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network

• URL: http://arxiv.org/abs/2112.10683v1
• Date: Mon, 20 Dec 2021 17:04:00 GMT
• Title: SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network
• Authors: Xianfang Zeng, Jiangning Zhang, Liang Liu, Guangzhong Tian, Yong Liu
• Abstract summary: We propose a novel domain-adaptive degradation network for face super-resolution in the wild. Our model achieves state-of-the-art performance on both CelebA and real-world face dataset.
• Score: 12.976199676093442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In spite of the success on benchmark datasets, most advanced face super-resolution models perform poorly in real scenarios since the remarkable domain gap between the real images and the synthesized training pairs. To tackle this problem, we propose a novel domain-adaptive degradation network for face super-resolution in the wild. This degradation network predicts a flow field along with an intermediate low resolution image. Then, the degraded counterpart is generated by warping the intermediate image. With the preference of capturing motion blur, such a model performs better at preserving identity consistency between the original images and the degraded. We further present the self-conditioned block for super-resolution network. This block takes the input image as a condition term to effectively utilize facial structure information, eliminating the reliance on explicit priors, e.g. facial landmarks or boundary. Our model achieves state-of-the-art performance on both CelebA and real-world face dataset. The former demonstrates the powerful generative ability of our proposed architecture while the latter shows great identity consistency and perceptual quality in real-world images.

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