GAN-based multi-style photo cartoonization

Shu, Yezhi, Yi, Ran, Xia, Mengfei, Ye, Zipeng, Zhao, Wang, Chen, Yang, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Liu, Yong-Jin 2022. GAN-based multi-style photo cartoonization. IEEE Transactions on Visualization and Computer Graphics 28 (10) , pp. 3376-3390. 10.1109/TVCG.2021.3067201

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Abstract

Cartoon is a common form of art in our daily life and automatic generation of cartoon images from photos is highly desirable. However, state-of-the-art single-style methods can only generate one style of cartoon images from photos and existing multi-style image style transfer methods still struggle to produce high-quality cartoon images due to their highly simplified and abstract nature. In this paper, we propose a novel multi-style generative adversarial network (GAN) architecture, called MS-CartoonGAN, which can transform photos into multiple cartoon styles. We develop a multi-domain architecture, where the generator consists of a shared encoder and multiple decoders for different cartoon styles, along with multiple discriminators for individual styles. By observing that cartoon images drawn by different artists have their unique styles while sharing some common characteristics, our shared network architecture exploits the common characteristics of cartoon styles, achieving better cartoonization and being more efficient than single-style cartoonization. We show that our multi-domain architecture can theoretically guarantee to output desired multiple cartoon styles. Through extensive experiments including a user study, we demonstrate the superiority of the proposed method, outperforming state-of-the-art single-style and multi-style image style transfer methods.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISSN:	1077-2626
Date of First Compliant Deposit:	20 April 2021
Date of Acceptance:	15 March 2021
Last Modified:	05 Dec 2024 19:45
URI:	https://orca.cardiff.ac.uk/id/eprint/140553

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