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In this paper, we explore open-domain sketch-to-photo translation, which aimsto synthesize a realistic photo from a freehand sketch with its class label,even if the sketches of that class are missing in the training data. It ischallenging due to the lack of training supervision and the large geometricdistortion between the freehand sketch and photo domains. To synthesize theabsent freehand sketches from photos, we propose a framework that jointlylearns sketch-to-photo and photo-to-sketch generation. However, the generatortrained from fake sketches might lead to unsatisfying results when dealing withsketches of missing classes, due to the domain gap between synthesized sketchesand real ones. To alleviate this issue, we further propose a simple yeteffective open-domain sampling and optimization strategy to "fool" thegenerator into treating fake sketches as real ones. Our method takes advantageof the learned sketch-to-photo and photo-to-sketch mapping of in-domain dataand generalizes it to the open-domain classes. We validate our method on theScribble and SketchyCOCO datasets. Compared with the recent competing methods,our approach shows impressive results in synthesizing realistic color, texture,and maintaining the geometric composition for various categories of open-domainsketches. Our code is available at https://github.com/Mukosame/AODA

Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis