The use of deep learning technology in dance movement generation - Liu, Xin; Ko, Young Chun (Frontiers in Neurorobotics, 2022)
The dance generated by the traditional music action matching and statistical mapping models is less consistent with the music itself. Moreover, new dance movements cannot be generated. A dance movement generation algorithm based on deep learning is designed to extract the mapping between sound and motion features to solve these problems. First, the sound and motion features are extracted from music and dance videos, and then the model is built. In addition, a generator module, a discriminator module and a self-encoder module are added to make the dance movement smoother and consistent with the music. The Pix2PixHD model is used to transform the dance pose sequence into a real version of the dance. Finally, the experiment takes the dance video on the network as the training data, and trained 5000 times. 80% of the dance data are used as the training set and 20% as the test set. The experimental results show that Train, Valid, and Test values based on the Generator+Discriminator+Autoencoder model are 15.36, 17.19 and 19.12, respectively. The similarity between the generated dance sequence and the real dance sequence is 0.063, which shows that the proposed model can generate a dance more in line with the music. Moreover, the generated dance posture is closer to the real dance posture. The discussion has certain reference value for intelligent dance teaching, game field, cross-modal generation and exploring the relationship between audio-visual information.