  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.
