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Guided neural style transfer for shape stylization

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  • Gantugs Atarsaikhan
  • Brian Kenji Iwana
  • Seiichi Uchida

Abstract

Designing logos, typefaces, and other decorated shapes can require professional skills. In this paper, we aim to produce new and unique decorated shapes by stylizing ordinary shapes with machine learning. Specifically, we combined parametric and non-parametric neural style transfer algorithms to transfer both local and global features. Furthermore, we introduced a distance-based guiding to the neural style transfer process, so that only the foreground shape will be decorated. Lastly, qualitative evaluation and ablation studies are provided to demonstrate the usefulness of the proposed method.

Suggested Citation

  • Gantugs Atarsaikhan & Brian Kenji Iwana & Seiichi Uchida, 2020. "Guided neural style transfer for shape stylization," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-23, June.
    • Handle: RePEc:plo:pone00:0233489
    DOI: 10.1371/journal.pone.0233489
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