  Face photo-sketch synthesis aims at generating a facial sketch/photo
conditioned on a given photo/sketch. It is of wide applications including
digital entertainment and law enforcement. Precisely depicting face
photos/sketches remains challenging due to the restrictions on structural
realism and textural consistency. While existing methods achieve compelling
results, they mostly yield blurred effects and great deformation over various
facial components, leading to the unrealistic feeling of synthesized images. To
tackle this challenge, in this work, we propose to use the facial composition
information to help the synthesis of face sketch/photo. Specially, we propose a
novel composition-aided generative adversarial network (CA-GAN) for face
photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face
photo/sketch and the corresponding pixel-wise face labels for generating a
sketch/photo. In addition, to focus training on hard-generated components and
delicate facial structures, we propose a compositional reconstruction loss.
Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add
compelling details. Experimental results show that our method is capable of
generating both visually comfortable and identity-preserving face
sketches/photos over a wide range of challenging data. Our method achieves the
state-of-the-art quality, reducing best previous Frechet Inception distance
(FID) by a large margin. Besides, we demonstrate that the proposed method is of
considerable generalization ability. We have made our code and results publicly
available: https://fei-hdu.github.io/ca-gan/.
