sign-lang@LREC Anthology

Analysis of Torso Movement for Signing Avatar Using Deep Learning

Choudhury, Shatabdi


Volume:
Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives
Venue:
Marseille, France
Date:
24 June 2022
Pages:
7–12
Publisher:
European Language Resources Association (ELRA)
License:
CC BY-NC 4.0
ACL ID:
2022.sltat-1.2
ISBN:
979-10-95546-82-5

Content Categories

Languages:
French Sign Language
Corpora:
MOCAP1
Avatars:
Paula

Abstract

Avatars are virtual or on-screen representations of a human used in various roles for sign language display, including translation and educational tools. Though the ability of avatars to portray acceptable sign language with believable human-like motion has improved in recent years, many still lack the naturalness and supporting motions of human signing. Such details are generally not included in the linguistic annotation. Nevertheless, these motions are highly essential to displaying lifelike and communicative animations. This paper presents a deep learning model for use in a signing avatar. The study focuses on coordinating torso movements and other human body parts. The proposed model will automatically compute the torso rotation based on the avatar's wrist positions. The resulting motion can improve the user experience and engagement with the avatar.

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BibTeX Export

@inproceedings{choudhury:70011:sltat:lrec,
  author    = {Choudhury, Shatabdi},
  title     = {Analysis of Torso Movement for Signing Avatar Using Deep Learning},
  pages     = {7--12},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee},
  booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {24},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-82-5},
  language  = {english},
  url       = {http://www.lrec-conf.org/proceedings/lrec2022/workshops/sltat/pdf/2022.sltat-1.2}
}
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