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In spite of the success on benchmark datasets, most advanced facesuper-resolution models perform poorly in real scenarios since the remarkabledomain gap between the real images and the synthesized training pairs. Totackle this problem, we propose a novel domain-adaptive degradation network forface super-resolution in the wild. This degradation network predicts a flowfield along with an intermediate low resolution image. Then, the degradedcounterpart is generated by warping the intermediate image. With the preferenceof capturing motion blur, such a model performs better at preserving identityconsistency between the original images and the degraded. We further presentthe self-conditioned block for super-resolution network. This block takes theinput image as a condition term to effectively utilize facial structureinformation, eliminating the reliance on explicit priors, e.g. facial landmarksor boundary. Our model achieves state-of-the-art performance on both CelebA andreal-world face dataset. The former demonstrates the powerful generativeability of our proposed architecture while the latter shows great identityconsistency and perceptual quality in real-world images.

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