Toward Realistic Face Photo–Sketch Synthesis via Composition-Aided GANs
Face photo–sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It covers 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 article, we propose using facial composition information to help the synthesis of face sketch/photo. Especially, 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 pixelwise face labels for generating a sketch/photo. Next, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. In addition, we employ a perceptual loss function to encourage the synthesized image and real image to be perceptually similar. Finally, we use stacked CA-GANs (SCA-GANs) to further rectify defects and add compelling details. The 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. In addition, our method significantly decreases the best previous Fréchet inception distance (FID) from 36.2 to 26.2 for sketch synthesis, and from 60.9 to 30.5 for photo synthesis. Besides, we demonstrate that the proposed method is of considerable generalization ability.