Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous style transfer and shape exaggeration with rich diversity, and meanwhile preserving the identity of the input. To address this challenging problem, we propose a novel framework called Multi-Warping GAN (MW-GAN), including a style network and a geometric network that are designed to conduct style transfer and geometric exaggeration respectively. We bridge the gap between the style and landmarks of an image with corresponding latent code spaces by a dual way design, so as to generate caricatures with arbitrary styles and geometric exaggeration, which can be specified either through random sampling of latent code or from a given caricature sample. Besides, we apply identity preserving loss to both image space and landmark space, leading to a great improvement in quality of generated caricatures. Experiments show that caricatures generated by MW-GAN have better quality than existing methods.

中文翻译：

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MW-GAN：用于多样式几何夸张漫画生成的多扭曲 GAN

给定输入的人脸照片，漫画生成的目标是生成与照片具有相同身份的风格化、夸张的漫画。它需要同时具有丰富多样性的风格转移和形状夸张，同时保留输入的身份。为了解决这个具有挑战性的问题，我们提出了一种称为 Multi-Warping GAN (MW-GAN) 的新框架，包括一个风格网络和一个几何网络，分别设计用于进行风格转移和几何夸张。我们通过双重设计将图像的风格和地标与相应的潜在代码空间之间的差距缩小，从而生成具有任意风格和几何夸张的漫画，可以通过潜在代码的随机采样或给定的指定漫画样本。除了，我们将身份保留损失应用于图像空间和地标空间，从而大大提高了生成漫画的质量。实验表明，MW-GAN 生成的漫画比现有方法具有更好的质量。