Improved Facial Realism through an Enhanced Representation of Anatomical Behavior in Sign Language Avatars

Ronan Johnson


Abstract
Facial movements and expressions are critical features of signed languages, yet are some of the most challenging to reproduce on signing avatars. Due to the relative lack of research efforts in this area, the facial capabilities of such avatars have yet to receive the approval of those in the Deaf community. This paper revisits the representations of the human face in signed avatars, specifically those based on parameterized muscle simulation such as FACS and the MPEG-4 file definition. An improved framework based on rotational pivots and pre-defined movements is capable of reproducing realistic, natural gestures and mouthings on sign language avatars. The new approach is more harmonious with the underlying construction of signed avatars, generates improved results, and allows for a more intuitive workflow for the artists and animators who interact with the system.
Anthology ID:
2022.sltat-1.8
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
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Eleni Efthimiou, Stavroula-Evita Fotinea, Thomas Hanke, John C. McDonald, Dimitar Shterionov, Rosalee Wolfe
Venue:
SLTAT
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
53–58
Language:
URL:
https://aclanthology.org/2022.sltat-1.8
DOI:
Bibkey:
Cite (ACL):
Ronan Johnson. 2022. Improved Facial Realism through an Enhanced Representation of Anatomical Behavior in Sign Language Avatars. In Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives, pages 53–58, Marseille, France. European Language Resources Association.
Cite (Informal):
Improved Facial Realism through an Enhanced Representation of Anatomical Behavior in Sign Language Avatars (Johnson, SLTAT 2022)
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PDF:
https://aclanthology.org/2022.sltat-1.8.pdf