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Automatic high-quality rendering of anime scenes from complex real-worldimages is of significant practical value. The challenges of this task lie inthe complexity of the scenes, the unique features of anime style, and the lackof high-quality datasets to bridge the domain gap. Despite promising attempts,previous efforts are still incompetent in achieving satisfactory results withconsistent semantic preservation, evident stylization, and fine details. Inthis study, we propose Scenimefy, a novel semi-supervised image-to-imagetranslation framework that addresses these challenges. Our approach guides thelearning with structure-consistent pseudo paired data, simplifying the pureunsupervised setting. The pseudo data are derived uniquely from asemantic-constrained StyleGAN leveraging rich model priors like CLIP. Wefurther apply segmentation-guided data selection to obtain high-quality pseudosupervision. A patch-wise contrastive style loss is introduced to improvestylization and fine details. Besides, we contribute a high-resolution animescene dataset to facilitate future research. Our extensive experimentsdemonstrate the superiority of our method over state-of-the-art baselines interms of both perceptual quality and quantitative performance.

Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation