TY - GEN
T1 - Dancing with turks
AU - Chiang, I. Kao
AU - Spiro, Ian
AU - Lee, Seungkyu
AU - Lees, Alyssa
AU - Liu, Jingchen
AU - Bregler, Chris
AU - Liu, Yanxi
N1 - Funding Information:
This work is supported in part by NSF grants IIS-1248076 and IIS-1144938.
Publisher Copyright:
© 2015 ACM.
PY - 2015/10/13
Y1 - 2015/10/13
N2 - Dance is a dynamic art form that reects a wide range of cultural diversity and individuality. With the advancement of motion-capture technology combined with crowd-sourcing and machine learning algorithms, we explore the complex relationship between perceived dance quality/dancer's gender and dance movements/music respectively. As a feasibility study, we construct a computational framework for an analysis-synthesis-feedback loop using a novel multimedia dance-music texture representation. Furthermore, we integrate crowd-sourcing, music and motion-capture data, and machine learning-based methods for dance segmentation, analysis and synthesis of new dancers. A quantitative validation of this framework on a motion-capture dataset of 172 dancers evaluated by more than 400 independent on-line raters demonstrates significant correlation between human perception and the algorithmically intended dance quality or gender of synthesized dancers. The technology illustrated in this work has a high potential to advance the multimedia entertainment industry via dancing with Turks.
AB - Dance is a dynamic art form that reects a wide range of cultural diversity and individuality. With the advancement of motion-capture technology combined with crowd-sourcing and machine learning algorithms, we explore the complex relationship between perceived dance quality/dancer's gender and dance movements/music respectively. As a feasibility study, we construct a computational framework for an analysis-synthesis-feedback loop using a novel multimedia dance-music texture representation. Furthermore, we integrate crowd-sourcing, music and motion-capture data, and machine learning-based methods for dance segmentation, analysis and synthesis of new dancers. A quantitative validation of this framework on a motion-capture dataset of 172 dancers evaluated by more than 400 independent on-line raters demonstrates significant correlation between human perception and the algorithmically intended dance quality or gender of synthesized dancers. The technology illustrated in this work has a high potential to advance the multimedia entertainment industry via dancing with Turks.
UR - http://www.scopus.com/inward/record.url?scp=84962890389&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962890389&partnerID=8YFLogxK
U2 - 10.1145/2733373.2806220
DO - 10.1145/2733373.2806220
M3 - Conference contribution
AN - SCOPUS:84962890389
T3 - MM 2015 - Proceedings of the 2015 ACM Multimedia Conference
SP - 241
EP - 250
BT - MM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PB - Association for Computing Machinery, Inc
T2 - 23rd ACM International Conference on Multimedia, MM 2015
Y2 - 26 October 2015 through 30 October 2015
ER -