S5: Sketch-to-image Synthesis via Scene and Size Sensing

Sketch-to-image synthesis method transforms a simple abstract black-and-white sketch into an image. Most sketch-to-image synthesis methods generate an image in an end-to-end manner, leading to generate a non-satisfactory result. The reason is that, in end-to-end models, the models generate images directly from the input sketches. Thus, with very abstract and complicated sketches, the models might struggle in generating naturalistic images due to the simultaneous focus on both factors: overall shape and fine-grained details. In this paper, we propose to divide the problem into subproblems. To this end, an intermediate output, which is a semantic mask map, is first generated from the input sketch via an instance and semantic segmentation. In the instance segmentation stage, the objects' sizes might be modified depending on the surrounding environment and their respective size prior to reflect reality and produce more realistic images. In the semantic seg-mentation stage, a background segmentation is first constructed based on the context of the detected objects. Various natural scenes are implemented for both indoor and outdoor scenes. Following this, a foreground segmentation process is commenced, where each detected object is semantically added into the constructed segmented background. Then, in the next stage, an image-to-image translation model is leveraged to convert the semantic mask map into a colored image. Finally, a post-processing stage is incorporated to further enhance the image result. Extensive experiments demonstrate the superiority of our proposed method over state-of-the-art methods.