TileGAN: Synthesis of large-scale non-homogeneous textures

Anna Frühstück, Ibraheem Alhashim, Peter Wonka

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We build on recent generative adversarial networks and propose two extensions in this paper. First, we propose an algorithm to combine outputs of GANs trained on a smaller resolution to produce a large-scale plausible texture map with virtually no boundary artifacts. Second, we propose a user interface to enable artistic control. Our quantitative and qualitative results showcase the generation of synthesized high-resolution maps consisting of up to hundreds of megapixels as a case in point.
Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalACM Transactions on Graphics
Issue number4
StatePublished - Jan 1 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): URF/1/3426-01-01
Acknowledgements: We would like to thank Tero Karras and his collaborators [2018a] for making their source code available. This work was supported by the KAUST Office of Sponsored Research (OSR) under Award No. URF/1/3730-01-01 and URF/1/3426-01-01.


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