MCLGAN: a multi-style cartoonization method based on style condition information

Abstract

Image cartoonization, a special kind of style transformation, is a challenging image processing task. Most existing cartoonization methods aim at single-style transformation. While multiple models are trained to achieve multi-style transformation, which is time-consuming and resource-consuming. Meanwhile, existing multi-style cartoonization methods based on generative adversarial network require multiple discriminators to handle different styles, which increases the complexity of the network. To solve the above issues, this paper proposes an image cartoonization method for multi-style transformation based on style condition information, called MCLGAN. This approach integrates two key components for promoting multi-style image cartoonization. Firstly, we design a conditional generator and a multi-style learning discriminator to embed the style condition information into the feature space, so as to enhance the ability of the model in realizing different cartoon styles. Then the new loss mechanism, the conditional contrastive loss, is used strategically to strengthen the difference between different styles, thus effectively realizing multi-style image cartoonization. At the same time, MCLGAN simplifies the cartoonization process of different styles images, and only needs to train the model once, which significantly improves the efficiency. Numerous experiments verify the validity of our method as well as demonstrate the superiority of our method compared to previous methods.