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 $\mathcal{ X}$ and the sketch domain $Y$ by using the line-drawing domain $\mathcal{ Z}$. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. $F: \mathcal{ X}/\mathcal{ Y} \mapsto \mathcal{ Z}$. Consequently, we obtain \textit{ pseudo paired data} $(\mathcal{ Z}, \mathcal{ Y})$, and can learn the mapping $G:\mathcal{ Z} \mapsto \mathcal{ Y}$ 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.
Categories: cs.CV