  In this paper, we explore the open-domain sketch-to-photo translation, which
aims to 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
is challenging due to the lack of training supervision and the large geometry
distortion between the freehand sketch and photo domains. To synthesize the
absent freehand sketches from photos, we propose a framework that jointly
learns sketch-to-photo and photo-to-sketch generation. However, the generator
trained from fake sketches might lead to unsatisfying results when dealing with
sketches of missing classes, due to the domain gap between synthesized sketches
and real ones. To alleviate this issue, we further propose a simple yet
effective open-domain sampling and optimization strategy to "fool" the
generator into treating fake sketches as real ones. Our method takes advantage
of the learned sketch-to-photo and photo-to-sketch mapping of in-domain data
and generalizes them to the open-domain classes. We validate our method on the
Scribble 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-domain
sketches.
