论文标题
MW-GAN：多型漫画生成的多射线，多式夸张
MW-GAN: Multi-Warping GAN for Caricature Generation with Multi-Style Geometric Exaggeration
论文作者
论文摘要
鉴于输入面照片，漫画一代的目的是产生与照片相同的身份的风格化，夸张的漫画。它需要具有丰富多样性的同时风格转移和形状夸张，并保留输入的身份。为了解决这个具有挑战性的问题，我们提出了一个名为“多枪gan”（MW-GAN）的新颖框架，包括样式网络和旨在分别进行样式转移和几何夸张的几何网络。我们通过双重方式设计弥合图像的样式和地标之间的差距，以相应的潜在代码空间设计，以生成具有任意样式和几何夸张的漫画，可以通过随机抽样潜在的代码或从给定的漫画样本中指定。此外，我们对图像空间和地标空间都应用了身份损失，从而极大地提高了产生的漫画质量。实验表明，MW-GAN产生的漫画比现有方法具有更好的质量。
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.
