Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm. In this paper, we propose the first Generative Adversarial Network (GAN) for unpaired photo-to-caricature translation, which we call "CariGANs". It explicitly models geometric exaggeration and appearance stylization using two components: CariGeoGAN, which only models the geometry-to-geometry transformation from face photos to caricatures, and CariStyGAN, which transfers the style appearance from caricatures to face photos without any geometry deformation. In this way, a difficult cross-domain translation problem is decoupled into two easier tasks. The perceptual study shows that caricatures generated by our CariGANs are closer to the hand-drawn ones, and at the same time better persevere the identity, compared to state-of-the-art methods. Moreover, our CariGANs allow users to control the shape exaggeration degree and change the color/texture style by tuning the parameters or giving an example caricature.

本文提出了第一个基于生成对抗网络的CariGANs来进行非成对的照片到漫画的转换，它明确地模拟了几何夸张和外观风格化，并将难以跨领域转换问题分解为两个更容易的任务，最终经过感性研究证明CariGANs所生成的漫画与手绘漫画更接近，并更好地保留了身份，同时允许用户通过调整参数或提供样例来控制形状夸张程度和更改颜色/纹理风格。

CariGANs: 无配对照片到卡通画的翻译