Dance choreography is an intense, creative and intuitive process. A choreographer has to finalize appropriate dance steps from amongst millions of possibilities. Though it is not impossible, the choreographer being human cannot explore, analyze and remember all these variations among steps due to large scale of available options. Hence, we propose to simplify the problem of exploring and selecting dance steps from amongst the huge set of all possible variations for an Indian Classical Dance, BharataNatyam (BN). Based on a computational model developed by Jadhav et al.[13], we propose a Genetic Algorithm (GA) driven automatic system that would provide a list of unexplored novel dance steps to choreographers. We have incorporated certain measures to ensure that the proposed dance steps should be feasible and appropriate.In this paper, we discuss an automated approach to obtain unexplored dance steps using a proposed fitness function for a single beat/count. The details of experimental study performed for the Genetic Algorithm based art to SMart (System Modelled art) system along with the results obtained are also presented in this paper.
BharataNatyam (BN) like any other Indian classical dance comprises of a sequence of possible and legitimate dance steps. It is estimated that using the main body parts namely head, neck, hand and leg itself, more than 5 lakh dance steps can be generated for a single beat. Choreographers and even dancers usually repeat their favorite dance steps or the conventional casual dance steps taught by their teacher while performing for multiple beats. As a result several valid and many other significant non-traditional dance steps remain unexplored. Hence, we propose to have an auto enumeration followed by auto classification of significant BN dance steps that can be used in dance performance and choreography. In short, we try to transform sheer art into a System Modelled art i.e. 'Art to SMart'. The foremost and most challenging task is to have a computational model that represents different BN dance poses.In this paper, we have proposed a computational model to represent BN dance steps and have presented the detailed description of formulation of a dance position vector that comprises of thirty explicitly identified attributes to capture and represent all variations of a BN dance step. We have named it as a SMart system for modelling BN steps, where SMart stands for System Modelled art .We have also demonstrated sample dance steps and their corresponding representations with appropriate dance step images.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.