Skeleton modelling for the realistic generation of french sign language postures

Recent progresses in animation have allowed the use of virtual character to many extents. Virtual signers (or signing avatars) are three-dimension characters expressing themselves in sign language. These characters allow the the broadcasting of audio information to deaf and hearing-impaired signers in an anonymous and modular fashion. However, automatic generation of animation for such characters strongly rely on the lexical description of signs. Signs described through these models are usually perfect and geometric performances leading to robotic and unrealistic movements. These thesis focuses on adding information to the control skeleton of the signer to help him perform signs in a more human and realistic way. Such information are grouped under the name of "anatomic model" and are divided in five main contributions : a new computer-based description of the skeleton, an anthropometric study of the hand, the merging of articulatory dependencies, a new model of the carpo-metacarpal complex allowing an easier opposition of the thumb and finally a model computing posture comfort. These contributions are then implemented in a generation system through linguistic contraints-adapted techniques. The study ends with an evaluation of the system and a the presentation of future prospects.

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Source https://theses.hal.science/tel-00672085
Author Delorme, Maxime
Maintainer CCSD
Last Updated May 27, 2026, 22:25 (UTC)
Created May 27, 2026, 22:25 (UTC)
Identifier NNT: 2011PA112312
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI) ; Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919) ; Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE)
creator Delorme, Maxime
date 2011-12-07T00:00:00
harvest_object_id c40b9ba3-137a-4bd0-aaf1-4ba8f511b342
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-04-17T00:00:00
set_spec type:THESE