Computational Model of Human Creativity in Dance Choreography

Dance choreography is a system of techniques used to create new dances. The choreographer devises body movements using internal and external cues to express feelings and concepts, from the most abstract ideas to very concrete human situations in a highly creative manner. In 3D Tele-immersive Environments (3DTI) the choreographer has exponentially more options to create new body movements in the new dance since the 3DTI technology offers an array of visual stimulations, called Digital Options, which influence this movement making process. In this paper, first, we explore the creative process of dance choreography through Laban/Bartenieff Movement Analysis (LMA) representation via computational models in the 3D technology. Second, we elaborate on the creativity framework and the design of dynamic compositions that are placed in the geographically distributed multi-stream, multiparty 3DTI environment. Third, we discuss some very preliminary results of our creativity framework and first findings that validate parts of our computational modeling and 3DTI design.

[1]  Takeo Kanade,et al.  A real time system for robust 3D voxel reconstruction of human motions , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Oliver Schreer,et al.  An immersive 3D video-conferencing system using shared virtual team user environments , 2002, CVE '02.

[4]  A.D. Kuo,et al.  An optimal control model for analyzing human postural balance , 1995, IEEE Transactions on Biomedical Engineering.

[5]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Ruzena Bajcsy,et al.  Learning Physical Activities in Immersive Virtual Environments , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[7]  Y. Aloimonos,et al.  Complete calibration of a multi-camera network , 2000, Proceedings IEEE Workshop on Omnidirectional Vision (Cat. No.PR00704).

[8]  Takeo Kanade,et al.  A stereo machine for video-rate dense depth mapping and its new applications , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Thomas Malzbender,et al.  Understanding performance in coliseum, an immersive videoconferencing system , 2005, TOMCCAP.

[10]  Colin Beardon,et al.  New Visions In Performance: The Impact of Digital Technologies , 2005 .

[11]  Ruzena Bajcsy,et al.  TEEVE: the next generation architecture for tele-immersive environments , 2005, Seventh IEEE International Symposium on Multimedia (ISM'05).

[12]  Oliver Grau,et al.  The Robot in the Garden: Telerobotics and Telepistemology in the Age of the Internet , 2000 .

[13]  Ramesh Raskar,et al.  Image-based visual hulls , 2000, SIGGRAPH.

[14]  Takeo Kanade,et al.  Virtual Space Teleconferencing Using a Sea of Cameras , 1994 .

[15]  Ruggero Frezza,et al.  Control of a Manipulator with a Minimum Number of Motion Primitives , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[16]  Klara Nahrstedt,et al.  Dynamic QoS-aware multimedia service configuration in ubiquitous computing environments , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[17]  David J. Brady,et al.  Information flow in streaming 3D video , 2001, Optics East.