Cartoon-Flow: A Flow-Based Generative Adversarial Network for Arbitrary-Style Photo Cartoonization

Photo cartoonization aims to convert photos of real-world scenes into cartoon-style images. Recently, generative adversarial network (GAN)-based methods for photo cartoonization have been proposed to generate pleasable cartoonized images. However, as these methods can transfer only learned cartoon styles to photos, they are limited in general-purpose applications where unlearned styles are often required. To address this limitation, an arbitrary style transfer (AST) method that transfers arbitrary artistic style into content images can be used. However, conventional AST methods do not perform satisfactorily in cartoonization for two reasons. First, they cannot capture the unique characteristics of cartoons that differ from common artistic styles. Second, they suffer from content leaks in which the semantic structure of the content is distorted. In this paper, to solve these problems, we propose a novel arbitrary-style photo cartoonization method, Cartoon-Flow. More specifically, we construct a new hybrid GAN with an invertible neural flow generator to effectively preserve content information. In addition, we introduce two new losses for cartoonization: (1) edge-promoting smooth loss to learn the unique characteristics of cartoons with smooth surfaces and clear edges, and (2) line loss to mimic the line drawing of cartoons. Extensive experiments demonstrate that the proposed method outperforms previous methods both quantitatively and qualitatively.