论文标题
面部漫画化的跨域样式混合
Cross-Domain Style Mixing for Face Cartoonization
论文作者
论文摘要
卡通域最近越来越受欢迎。先前的研究已尝试将高质量的肖像风格化到卡通域中。但是，这构成了一个巨大的挑战，因为它们没有正确解决关键限制，例如需要大量的训练图像或缺乏对抽象卡通面孔的支持。最近，已使用层交换方法用于定型，仅需要有限数量的培训图像。但是，其用例仍然很狭窄，因为它继承了其余问题。在本文中，我们提出了一种称为跨域样式混合的新颖方法，该方法结合了两个来自两个不同领域的潜在代码。我们的方法有效地将面孔定型为在各种面部抽象级别的多个卡通字符中，仅使用一个发电机，甚至不使用大量训练图像。
Cartoon domain has recently gained increasing popularity. Previous studies have attempted quality portrait stylization into the cartoon domain; however, this poses a great challenge since they have not properly addressed the critical constraints, such as requiring a large number of training images or the lack of support for abstract cartoon faces. Recently, a layer swapping method has been used for stylization requiring only a limited number of training images; however, its use cases are still narrow as it inherits the remaining issues. In this paper, we propose a novel method called Cross-domain Style mixing, which combines two latent codes from two different domains. Our method effectively stylizes faces into multiple cartoon characters at various face abstraction levels using only a single generator without even using a large number of training images.
