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Authors: Maroš Kollár ; Lukas Hudec and Wanda Benesova

Affiliation: Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovicova 2, Bratislava, Slovakia

Keyword(s): Texture, Synthesis, Multiclass, GAN, Controllability.

Abstract: Generative adversarial networks as a tool for generating content are currently one of the most popular methods for content synthesis. Despite its popularity, multiple solutions suffer from the drawback of a shortage of generality. It means that trained models can usually synthesize only one specific kind of output. The usual synthesis approach for generating N different texture species requires training N models with changing training data. However, few solutions explore the synthesis of multiple types of textures. In our work, we present an alternative approach for multiclass texture synthesis. We focus on the synthesis of realistic natural non-stationary textures. Our solution divides textures into classes based on the objects they represent and allows users to control the class of synthesized textures and their appearance. Thanks to the controllable selections from latent space, we also explore possibilities of creating transitions between classes of trained textures for potential better usage in applications where texture synthesis is required. (More)

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Paper citation in several formats:
Kollár, M.; Hudec, L. and Benesova, W. (2023). Multiclass Texture Synthesis Using Generative Adversarial Networks. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 87-97. DOI: 10.5220/0011657500003417

@conference{grapp23,
author={Maroš Kollár. and Lukas Hudec. and Wanda Benesova.},
title={Multiclass Texture Synthesis Using Generative Adversarial Networks},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP},
year={2023},
pages={87-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011657500003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP
TI - Multiclass Texture Synthesis Using Generative Adversarial Networks
SN - 978-989-758-634-7
IS - 2184-4321
AU - Kollár, M.
AU - Hudec, L.
AU - Benesova, W.
PY - 2023
SP - 87
EP - 97
DO - 10.5220/0011657500003417
PB - SciTePress