Multi-style cartoonization: Leveraging multiple datasets with generative adversarial networks

Abstract

Scene cartoonization aims to convert photos into stylized cartoons. While generative adversarial networks (GANs) can generate high-quality images, previous methods focus on individual images or single styles, ignoring relationships between datasets. We propose a novel multi-style scene cartoonization GAN that leverages multiple cartoon datasets jointly. Our main technical contribution is a multi-branch style encoder that disentangles representations to model styles as distributions over entire datasets rather than images. Combined with a multi-task discriminator and perceptual losses optimizing across collections, our model achieves state-of-the-art diverse stylization while preserving semantics. Experiments demonstrate that by learning from inter-dataset relationships, our method translates photos into cartoon images with improved realism and abstraction fidelity compared to prior arts, without iterative re-training for new styles.