Few-shot font style transfer between different languages

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

In this paper, we propose a novel model FTransGAN that can transfer font styles between different languages by observing only a few samples. The automatic generation of a new font library is a challenging task and has been attracting many researchers' interests. Most previous works addressed this problem by transferring the style of the given subset to the content of unseen ones. Nevertheless, they only focused on the font style transfer in the same language. In many tasks, we need to learn the font information from one language and then apply it to other languages. It's difficult for the existing methods to do such tasks. To solve this problem, we specifically design our network into a multi-level attention form to capture both local and global features of the style images. To verify the generative ability of our model, we construct an experimental font dataset which includes 847 fonts, each of them containing English and Chinese characters with the same style. Experimental results show that compared with the state-of-the-art models, our model generates 80.3% of all user preferred images.

本文言語英語
ホスト出版物のタイトルProceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ433-442
ページ数10
ISBN(電子版)9780738142661
DOI
出版ステータス出版済み - 1 2021
イベント2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 - Virtual, Online, 米国
継続期間: 1 5 20211 9 2021

出版物シリーズ

名前Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021

会議

会議2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
国/地域米国
CityVirtual, Online
Period1/5/211/9/21

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

引用スタイル