An Automatic Music-Driven Folk Dance Movements Generation Method Based on Sequence-To-Sequence Network

Abstract

Music-driven automatic dance movement generation has become a hot research topic in the field of computer vision and internet of things in the recent past. To address the problems of increasing loss of Chinese folk dance culture, high cost of manual choreography methods and requirements for professional background, this paper proposes an automatic generation method for folk dance movements. Firstly, the proposed method collects paired folk music and dance videos to construct a synchronized folk music–dance dataset, extracting music and dance features using a feature extraction tool and a multi-scale fusion high-resolution network, respectively. Afterward, a sequence-to-sequence network model is constructed and then trained based on music features and dance features to synthesize rhythmically matched dance sequences for new music clips. Finally, an easy-to-use and effective automatic folk dance choreography method is implemented. Experimental data show that the proposed method performs well in automatic folk dance generation and the generated dances have folk characteristics and match the rhythm of the given music.