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Sketch-based image synthesis is a challenging problem in computer graphics and vision. Existing approaches either require exact edge maps or rely on the retrieval of existing photographs, which limits their applications in real-world scenarios. Accordingly in this work, we propose a staged semi-supervised generative adversarial networks based method for sketch-to-image synthesis, which can directly generate realistic images from novice sketches. More specifically, we first adopt a conditional generative adversarial network (CGAN) to extract class-wise representations from unpaired images. These class-wise representations are then exploited and incorporated with another CGAN, which are used to generate realistic images from sketches. By incorporating the class-wise representations, our method can leverage both the general class information from unpaired images and the targeted object information from …