TY - GEN
T1 - Automatic sign dance synthesis from gesture-based sign language
AU - Iwamoto, Naoya
AU - Shum, Hubert P.H.
AU - Asahina, Wakana
AU - Morishima, Shigeo
N1 - Funding Information:
The project was supported in part by the Royal Society (Ref: IES\R2\181024), JST ACCEL (Grant number: JPMJAC1602), and JSPS KAKENHI (Grant number: JP17H06101 and JP19H01129).
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/10/28
Y1 - 2019/10/28
N2 - Automatic dance synthesis has become more and more popular due to the increasing demand in computer games and animations. Existing research generates dance motions without much consideration for the context of the music. In reality, professional dancers make choreography 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 music visualization, allowing people with hearing difficulties to understand and enjoy music.
AB - Automatic dance synthesis has become more and more popular due to the increasing demand in computer games and animations. Existing research generates dance motions without much consideration for the context of the music. In reality, professional dancers make choreography 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 music visualization, allowing people with hearing difficulties to understand and enjoy music.
KW - Dance
KW - Motion Synthesis
KW - Multiple Regression Analysis
KW - Sign Language
UR - http://www.scopus.com/inward/record.url?scp=85074814336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074814336&partnerID=8YFLogxK
U2 - 10.1145/3359566.3360069
DO - 10.1145/3359566.3360069
M3 - Conference contribution
AN - SCOPUS:85074814336
T3 - Proceedings - MIG 2019: ACM Conference on Motion, Interaction, and Games
BT - Proceedings - MIG 2019
A2 - Spencer, Stephen N.
PB - Association for Computing Machinery, Inc
T2 - 2019 ACM Conference on Motion, Interaction, and Games, MIG 2019
Y2 - 28 October 2019 through 30 October 2019
ER -