Guided neural style transfer for shape stylization

Gantugs Atarsaikhan, Brian Kenji Iwana, Seiichi Uchida

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numbere0233489
JournalPloS one
Volume15
Issue number6
DOIs
Publication statusPublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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