Multi-scale terrain texturing using generative adversarial networks

Jonathan Klein, Stefan Hartmann, Michael Weinmann, Dominik L. Michels

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We propose a novel, automatic generation process for detail maps that allows the reduction of tiling artifacts in real-time terrain rendering. This is achieved by training a generative adversarial network (GAN) with a single input texture and subsequently using it to synthesize a huge texture spanning the whole terrain. The low-frequency components of the GAN output are extracted, down-scaled and combined with the highfrequency components of the input texture during rendering. This results in a terrain texture that is both highly detailed and nonrepetitive, which eliminates the tiling artifacts without decreasing overall image quality. The rendering is efficient regarding both memory consumption and computational costs. Furthermore, it is orthogonal to other techniques for terrain texture improvements such as texture splatting and can directly be combined with them
Original languageEnglish (US)
Title of host publicationInternational Conference on Image and Vision Computing New Zealand
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)9781538642764
StatePublished - Jul 5 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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