Cross-Domain Style Mixing for Face Cartoonization

• URL: http://arxiv.org/abs/2205.12450v1
• Date: Wed, 25 May 2022 02:39:10 GMT
• Title: Cross-Domain Style Mixing for Face Cartoonization
• Authors: Seungkwon Kim, Chaeheon Gwak, Dohyun Kim, Kwangho Lee, Jihye Back, Namhyuk Ahn, Daesik Kim
• Abstract summary: 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.
• Score: 6.174413879403037
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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