Automatic Sign Dance Synthesis from Gesture-based Sign Language

Iwamoto, Naoya, Shum, Hubert, Asahina, Wakana and Morishima, Shigeo (2019) Automatic Sign Dance Synthesis from Gesture-based Sign Language. In: MIG 2019 : 12th annual ACM/SIGGRAPH conference on Motion, Interaction and Games, 28-30 Oct 2019, Newcastle upon Tyne, England. (In Press)

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Iwamoto et al - Automatic Sign Dance Synthesis from Gesture-based Sign Language AAM.pdf - Accepted Version
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Automatic dance synthesis has become more and more popular dueto the increasing demand in computer games and animations. Existingresearch generates dance motions without much considerationfor the context of the music. In reality, professional dancers makechoreography according to the lyrics and music features. In this research, we focus on a particular genre of dance known as sign dance, which combines gesture-based sign language with full body dance motion. We propose a system to automatically generate sign dance from a piece of music and its corresponding sign gesture. The core of the system is a Sign Dance Model trained by multiple regression analysis to represent the correlations between sign dance and sign gesture/music, as well as a set of objective functions to evaluate the quality of the sign dance. Our system can be applied to musicvisualization, allowing people with hearing difficulties to understand and enjoy music.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Motion Synthesis, Dance, Sign Language, Multiple Regression Analysis
Subjects: G400 Computer Science
G500 Information Systems
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 17 Sep 2019 08:16
Last Modified: 11 Oct 2019 13:16

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