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MW-GAN: Multi-warping GAN for caricature generation with multi-style geometric exaggeration

Hou, Haodi, Huo, Jing, Wu, Jing ORCID:, Lai, Yu-Kun ORCID: and Gao, Yang 2021. MW-GAN: Multi-warping GAN for caricature generation with multi-style geometric exaggeration. IEEE Transactions on Image Processing 30 , pp. 8644-8657. 10.1109/TIP.2021.3118984

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Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous style transfer and shape exaggeration with rich diversity, and meanwhile preserving the identity of the input. To address this challenging problem, we propose a novel framework called Multi-Warping GAN (MW-GAN), including a style network and a geometric network that are designed to conduct style transfer and geometric exaggeration respectively. We bridge the gap between the style/landmark space and their corresponding latent code spaces by a dual way design, so as to generate caricatures with arbitrary styles and geometric exaggeration, which can be specified either through random sampling of latent code or from a given caricature sample. Besides, we apply identity preserving loss to both image space and landmark space, leading to a great improvement in quality of generated caricatures. Experiments show that caricatures generated by MW-GAN have better quality than existing methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1057-7149
Date of First Compliant Deposit: 25 November 2021
Date of Acceptance: 16 September 2021
Last Modified: 07 Nov 2023 19:36

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