Landmark-guided deformation transfer of template facial expressions for automatic generation of avatar blendshapes

Hayato Onizuka, DIego Thomas, Hideaki Uchiyama, Rin Ichiro Taniguchi

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

1 被引用数 (Scopus)

抄録

Blendshape models are commonly used to track and re-target facial expressions to virtual avatars using RGB-D cameras and without using any facial marker. When using blendshape models, the target avatar model must possess a set of key-shapes that can be blended depending on the estimated facial expression. Creating realistic set of key-shapes is extremely difficult and requires time and professional expertise. As a consequence, blendshape-based re-targeting technology can only be used with a limited amount of pre-built avatar models, which is not attractive for the large public. In this paper, we propose an automatic method to easily generate realistic key-shapes of any avatar that map directly to the source blendshape model (the user is only required to select a few facial landmarks on the avatar mesh). By doing so, captured facial motion can be easily re-targeted to any avatar, even when the avatar has largely different shape and topology compared with the source template mesh. Our experimental results show the accuracy of our proposed method compared with the state-of-the-art method for mesh deformation transfer.

本文言語英語
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2100-2108
ページ数9
ISBN(電子版)9781728150239
DOI
出版ステータス出版済み - 10 2019
イベント17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, 大韓民国
継続期間: 10 27 201910 28 2019

出版物シリーズ

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

会議

会議17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
国/地域大韓民国
CitySeoul
Period10/27/1910/28/19

All Science Journal Classification (ASJC) codes

  • 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

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