Guided neural style transfer for shape stylization

Gantugs Atarsaikhan, Brian Kenji Iwana, Seiichi Uchida

研究成果: ジャーナルへの寄稿学術誌査読

5 被引用数 (Scopus)

抄録

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.

本文言語英語
論文番号e0233489
ジャーナルPloS one
15
6
DOI
出版ステータス出版済み - 6月 2020

!!!All Science Journal Classification (ASJC) codes

  • 一般

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