Research on Automatic Dance Generation System Based on Deep Learning

Abstract

How to model the mapping relationship between music and dance so that the model can automatically generate the corresponding dance based on the rhythm and style of music is the most important issue to be resolved in the automatic arrangement of music and dance. Due to the flexible movement of limbs and the change in camera angle, the visual information of various parts of the human body changes drastically in practical applications, posing significant difficulties in the generation of high-resolution target dance images. Based on deep learning, this paper proposes a two-way voluntary neural network (TWCNN) dance automatic generation system (DL). The long short-term memory (LSTM) network model is an action generation sub-network, which generates each frame of action based on the extracted high-level music features and the bottom-level action features from the previous moment. The recognition accuracy of the proposed algorithm is 12.4 percent and 6.8 percent better than that of the conventional depth CNN, according to the test results.