Regularized adversarial training for single-shot virtual try-on

Kotaro Kikuchi, Kota Yamaguchi, Edgar Simo-Serra, Tetsunori Kobayashi

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Spatially placing an object onto a background is an essential operation in graphic design and facilitates many different applications such as virtual try-on. The placing operation is formulated as a geometric inference problem for given foreground and background images, and has been approached by spatial transformer architecture. In this paper, we propose a simple yet effective regularization technique to guide the geometric parameters based on user-defined trust regions. Our approach stabilizes the training process of spatial transformer networks and achieves a high-quality prediction with single-shot inference. Our proposed method is independent of initial parameters, and can easily incorporate various priors to prevent different types of trivial solutions. Empirical evaluation with the Abstract Scenes and CelebA datasets shows that our approach achieves favorable results compared to baselines.

本文言語English
ホスト出版物のタイトルProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3149-3152
ページ数4
ISBN(電子版)9781728150239
DOI
出版ステータスPublished - 2019 10月
イベント17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of
継続期間: 2019 10月 272019 10月 28

出版物シリーズ

名前Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

Conference

Conference17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
国/地域Korea, Republic of
CitySeoul
Period19/10/2719/10/28

ASJC Scopus subject areas

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

引用スタイル