2020年6月
Guided neural style transfer for shape stylization
PLoS ONE
- ,
- ,
- 巻
- 15
- 号
- 6
- 記述言語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1371/journal.pone.0233489
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.
- リンク情報
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- DOI
- https://doi.org/10.1371/journal.pone.0233489
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/32497055
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086008095&origin=inward 本文へのリンクあり
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85086008095&origin=inward
- ID情報
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- DOI : 10.1371/journal.pone.0233489
- eISSN : 1932-6203
- PubMed ID : 32497055
- SCOPUS ID : 85086008095