This article presents an original method for automatic generation of sign language (SL) content by means of the animation of an avatar, with the aim of creating animations that respect as much as possible linguistic constraints while keeping bio-realistic properties. This method is based on the use of a domain-specific bilingual corpus richly annotated with timed alignments between SL motion capture data, text and hierarchical expressions from the framework called AZee at subsentential level. Animations representing new SL content are built from blocks of animations present in the corpus and adapted to the context if necessary. A smart blending approach has been designed that allows the concatenation, replacement and adaptation of original animation blocks. This approach has been tested on a tailored testset to show as a proof of concept its potential in comprehensibility and fluidity of the animation, as well as its current limits.
@inproceedings{dauriac:70002:sltat:lrec,
author = {Dauriac, Boris and Braffort, Annelies and Bertin-Lem{\'e}e, Elise},
title = {Example-based Multilinear Sign Language Generation from a Hierarchical Representation},
pages = {21--28},
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-1.4}
}