Abstract:
The ancient Indian classical dance form BharataNatyam (BN) can stay alive and dynamic by allowing innovative, experimental ideas. These comprise of a sequence of possible and legitimate dance steps, and it is estimated that using the main body parts, namely head, neck, hands and legs, more than five lakh dance steps can be generated for a single beat. Thus, dance choreography becomes an intensive, creative, and intuitive process. A choreographer has to finalize appropriate dance steps from among millions of possibilities. Though it is not impossible, the human choreographer cannot explore, analyze and remember all these variations among steps because of the large number of available options. Hence, we propose to develop an autoenumeration followed by autoclassification of significant BN dance steps that can be used in dance performance and choreography. The foremost and most challenging task is to have a computational model that represents different BN dance poses. The second task is to develop a genetic algorithm (GA)-driven automatic system that would provide choreographers a list of unexplored, novel dance steps to fit in a single beat. We designed Art to SMart as a system to model the dance art of BharataNatyam. This system generates dance poses. Furthermore, we have developed a stick figure generation module to help visualize the 30-attribute dance vector generated from the system. The results are evaluated using a mean opinion score measure.
