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Conference Papers Year : 2019

Retrieving Human Traits from Gesture in Sign Language : The Example of Gestural Identity

Abstract

Virtual signers (or signing avatars) play an important role in the accesibility of information in sign languages. They have been developed notably for their capability to anonymize the signer's appearance and to enable dynamic or interactive scenarios. Recording real movements thanks to motion capture provides human-like, realistic and comprehensible signing animations. However, such accurate systems may also convey extralinguistic information such as identity, gender or emotional state. In the present work, we want to address the problem of gestural identity in the context of animated agents in French Sign Language (LSF). On the one hand, person identification from signing motion is assessed through psychophysical experiments, using point-light displays. On the other hand, a computational framework is developed for the analysis of LSF motion in order to investigate which features are critical for identification. For some applications, determining these movement parameters will enable controlling the gestural human traits of virtual signers.
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Dates and versions

hal-02400930 , version 1 (16-12-2020)

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Félix Bigand, E. Prigent, Annelies Braffort. Retrieving Human Traits from Gesture in Sign Language : The Example of Gestural Identity. International Conference on Movement and Computing, Oct 2019, Tempe, United States. pp.1-4, ⟨10.1145/3347122.3359607⟩. ⟨hal-02400930⟩
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