Sketch portrait generation is of wide applications including digital entertainment and law enforcement. Despite the great progress achieved by existing face sketch generation methods, they mostly yield blurred effects and great deformation over various facial parts. In order to tackle this challenge, we propose a novel composition-aided generative adversarial network (CA-GAN) for sketch portrait generation. First, we utilize paired inputs including a face photo and the corresponding pixel-wise face labels for generating the portrait. 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 (stack-CA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating identity-preserving, sketch-realistic, and visually comfortable sketch portraits over a wide range of challenging data, and outperforms existing methods. Besides, our methods show considerable generalization ability.

本文提出一个基于面部构图信息的Composition-aided Generative Adversarial Network (CA-GAN)来生成面部素描/照片。通过使用成对的输入包括面部照片/素描和相应的面部标签来进行合成，最终使用堆叠的 CA-GANs (SCA-GAN)去修正缺陷并增加有吸引力的细节，实验结果表明，我们的方法能够在广泛的具有挑战性的数据范围内生成视觉舒适、保持特征的面部素描/照片，并且取得了最佳的生成效果。

通过组合辅助 GAN 实现逼真的人脸照片素描合成