Conditional Vector Graphics Generation for Music Cover Images

Ivan Jarsky; Valeria Efimova; Ilya Bizyaev; Andrey Filchenkov

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Paper

Conditional Vector Graphics Generation for Music Cover Images

Topics: Color and Texture Analyses; Deep Learning for Visual Understanding ; Generative AI; Machine Learning Technologies for Vision; Shape Representation and Matching

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2 VISAPP: VISAPP, 233-243, 2024 , Rome, Italy

Authors: Ivan Jarsky ; Valeria Efimova ; Ilya Bizyaev and Andrey Filchenkov

Affiliation: ITMO University, Kronverksky Pr. 49, St. Petersburg, Russia

Keyword(s): GAN, Image Generation, Vector Graphics.

Abstract: Generative Adversarial Networks (GAN) have motivated a rapid growth of the domain of computer image synthesis. As almost all the existing image synthesis algorithms consider an image as a pixel matrix, the high-resolution image synthesis is complicated. A good alternative can be vector images. However, they belong to the highly sophisticated parametric space, which is a restriction for solving the task of synthesizing vector graphics by GANs. In this paper, we consider a specific application domain that softens this restriction dramatically allowing the usage of vector image synthesis. Music cover images should meet the requirements of Internet streaming services and printing standards, which imply high resolution of graphic materials without any additional requirements on the content of such images. Existing music cover image generation services do not analyze tracks themselves; however, some services mostly consider only genre tags. To generate music covers as vector images that re flect the music and consist of simple geometric objects, we suggest a GAN-based algorithm called CoverGAN. The assessment of resulting images is based on their correspondence to the music compared with AttnGAN and DALL-E text-to-image generation according to title or lyrics. Moreover, the significance of the patterns found by CoverGAN has been evaluated in terms of the correspondence of the generated cover images to the musical tracks. Listeners evaluate the music covers generated by the proposed algorithm as quite satisfactory and corresponding to the tracks. Music cover images generation code and demo are available at https://github.com/IzhanVarsky/CoverGAN. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:

Jarsky, I.; Efimova, V.; Bizyaev, I. and Filchenkov, A. (2024). Conditional Vector Graphics Generation for Music Cover Images. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 233-243. DOI: 10.5220/0012456100003660

@conference{visapp24,
author={Ivan Jarsky. and Valeria Efimova. and Ilya Bizyaev. and Andrey Filchenkov.},
title={Conditional Vector Graphics Generation for Music Cover Images},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={233-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012456100003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Conditional Vector Graphics Generation for Music Cover Images
SN - 978-989-758-679-8
IS - 2184-4321
AU - Jarsky, I.
AU - Efimova, V.
AU - Bizyaev, I.
AU - Filchenkov, A.
PY - 2024
SP - 233
EP - 243
DO - 10.5220/0012456100003660
PB - SciTePress