Music-Driven Choreography Based on Music Feature Clusters and Dynamic Programming

Generating choreography from music poses a significant challenge. Conventional dance generation methods are limited by only being able to match specific dance movements to music with corresponding rhythms, restricting the utilization of existing dance sequences. To address this limitation, we propose a method that generates a label, based on a probability distribution function derived from music features, that can be applied to music segments of varying lengths. By using the Kullback-Leibler divergence, we assess the similarity between music segments based on these labels. To ensure adaptability to different musical rhythms, we employ a cubic spline method to represent dance movements. This approach allows us to control the speed of a dance sequence by resampling it, enabling adaptation to varying rhythms based on the tempo of newly input music. To evaluate the effectiveness of our method, we compared the dances generated by our approach with those generated by other neural network-based and conventional methods. Quantitative evaluations demonstrated that our method outperforms these alternatives in terms of dance quality and fidelity.