SketchMan: Learning to Create Professional Sketches

Human free-hand sketches have been studied in various fields including sketch recognition, synthesis and sketch-based image retrieval. We propose a new challenging task sketch enhancement (SE) defined in an ill-posed space, i.e. enhancing a non-professional sketch (NPS) to a professional sketch (PS), which is a creative generation task different from sketch abstraction, sketch completion and sketch variation. For the first time we release a database of NPS with PS for anime characters. We cast sketch enhancement as an image-to-image translation problem by exploiting the relationship to corresponding intensive or sparse pixel domains for sketch domain. Specifically, we explore three different routines based on conditional generative adversarial network (cGAN), i.e. Sketch-Sketch (SS), Sketch-Colorization-Sketch (SCS) and Sketch-Abstraction-Sketch (SAS). SS is a one-stage model that directly maps NPS to PS, while SCS and SAS are two-stage models where auxiliary inputs, grayscale parsing and shape parsing, are involved. Multiple metrics are used to evaluate the performance of the models in both the sketch domain and other low-level feature domains. With quantitative and qualitative analysis of the experiments, we have established solid baselines, which, we hope, could encourage more research conducted on this task. Our dataset is publicly available via https://github.com/LCXCUC/SketchMan2020.