Generating artistic portrait drawings from images

Yi, Ran, Liu, Yong-Jin, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2024. Generating artistic portrait drawings from images. Lyu, Zhihan, ed. Applications of Generative AI, Springer, pp. 437-460. (10.1007/978-3-031-46238-2_22) Item availability restricted.

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Abstract

This chapter addresses generating artistic portrait drawings (APDrawings) from images, and we focus on two methods based on generative adversarial networks (GANs). We first introduce the genre of portrait line drawings, and review some existing methods for generating them from images. We also describe the Artistic Portrait Drawing (APDrawing) dataset, which contains 140 high-resolution face photos and corresponding portrait drawings executed by a professional artist. We then describe the APDrawingGAN method, which is a hierarchical GAN model that learns from paired data of face photos and portrait drawings, and the QMUPD method, which can learn from unpaired data of face photos and drawing. APDrawingGAN uses a novel distance transform loss to learn stroke lines in the drawings, and a local transfer loss to capture different drawing styles for different facial regions. QMUPD uses an asymmetric cycle mapping to preserve important facial features, and a quality metric to guide the generation towards high-quality drawings. We further introduce some recent developments which are based on multiple scale analysis, 3D information and multi-modal information. Finally, we describe the evaluation of artistic portrait drawings, which is a challenging task since there are many possible drawings that would be considered by experts to be acceptable.

Item Type:	Book Section
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISBN:	9783031462375
Date of First Compliant Deposit:	3 April 2024
Date of Acceptance:	6 March 2024
Last Modified:	08 Apr 2024 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/167688

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