  In this paper, we propose a novel framework based on deep neural networks for
face sketch synthesis from a photo. Imitating the process of how artists draw
sketches, our framework synthesizes face sketches in a cascaded manner. A
content image is first generated that outlines the shape of the face and the
key facial features. Textures and shadings are then added to enrich the details
of the sketch. We utilize a fully convolutional neural network (FCNN) to create
the content image, and propose a style transfer approach to introduce textures
and shadings based on a newly proposed pyramid column feature. We demonstrate
that our style transfer approach based on the pyramid column feature can not
only preserve more sketch details than the common style transfer method, but
also surpasses traditional patch based methods. Quantitative and qualitative
evaluations suggest that our framework outperforms other state-of-the-arts
methods, and can also generalize well to different test images. Codes are
available at https://github.com/chaofengc/Face-Sketch
