This project aims at the creation of a virtual humanoid endowed with expressive gestures. More specifically, we focus our attention on expressiveness (what type of gesture: fluidity, tension, anger) and on its semantic representations. Our approach relies on a data-driven animation scheme. From motion data captured thanks to an optical system and data gloves, we try to extract significant features of communicative gestures, and to re-synthesize them afterward with style variation. The proposed model is applied to the generation of a set of French sign language (FSL) gestures. Within this framework, a database involving the whole body, hands motion and facial expressions has been built The analysis of this database makes possible information retrieval about the semantics as well as the execution style of FSL gestures. These characteristics are integrated in gesture synthesis models qualitatively evaluated by their intelligibility and the realism of the produced animations.
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Rezzoug et al. (2006) studied this question.
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