Composition-Aided Face Photo-Sketch Synthesis

Face photo-sketch synthesis aims at generating a facial sketch (or photo) conditioned on a given photo (or sketch). It is of wide applications including digital entertainment and law enforcement. Despite the great progress achieved by existing methods, they mostly yield blurred effects and great deformation over various facial components. In order to tackle this challenge, 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. First, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating the sketch/photo. Second, we propose an improved pixel loss, termed compositional loss, to focus training on hard-generated components and delicate facial structures. Moreover, 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 identity-preserving and visually comfortable sketches and photos over a wide range of challenging data. Besides, cross-dataset photo-sketch synthesis evaluations demonstrate that the proposed method is of considerable generalization ability.