Differential Evolution Based Dance Composition

Abstract

In this chapter, we propose a novel approach in which a system autonomously composes dance sequences from previously taught dance moves with the help of the well-known differential evolution algorithm. In this chapter, we propose a novel approach in which a system autonomously composes dance sequences from previously taught dance moves with the help of the well-known differential evolution algorithm. Initially, we generated a large population of dance sequences. The fitness of each of these sequences was determined by calculating the total inter-move transition abruptness of the adjacent dance moves. The transition abruptness was calculated as the difference of corresponding slopes formed by connected body joint co-ordinates. By visually evaluating the dance sequences created, it was observed that the fittest dance sequence had the least abrupt inter-move transitions. Computer simulation undertaken revealed that the developed dance video frames do not have significant inter-move transition abruptness between two successive frames, indicating the efficacy of the proposed approach. Gestural data specific of dance moves is captured using a Microsoft Kinect sensor. The algorithm developed by us was used to fuse the dancing styles of various ‘Odissi’ dancers dancing to the same rasa (theme) and tala (beats) and loy (rhythm). In future, it may be used to fuse different forms of dance.

Contributed by Reshma Kar, Amit Konar and Aruna Chakraborty