  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.
