Bridging Unpaired Facial Photos And Sketches By Line-drawings

In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain X and the sketch domain Y by using the line-drawing domain Z. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. F: X/Y mapsto Z. Consequently, we obtain textitpseudo paired data (Z, Y), and can learn the mapping G:Z mapsto Y in a supervised learning manner. In the inference stage, given a facial photo, we can first transfer it to a line-drawing and then to a sketch by G circ F. Additionally, we propose a novel stroke loss for generating different types of strokes. Our method, termed sRender, accords well with human artists' rendering process. Experimental results demonstrate that sRender can generate multi-style sketches, and significantly outperforms existing unpaired image-to-image translation methods.