Guided neural style transfer for shape stylization
2020-06-04PLOS ONE 2020Code Available1· sign in to hype
Gantugs Atarsaikhan, Brian Kenji Iwana, Seiichi Uchida
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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.