Dance Evaluation Based on Movement and Neural Network

In terms of music-driven dance movement generation, the music movement matching model and the statistical mapping model have poor fit between the dance generated by the model and the music self. The generated dance movement is incomplete, and the smoothness and rationality of long-term dance sequences are low. The new dance moves and other related issues cannot be generated by the traditional model. In order to address these issues, we design a dance generation algorithm based on movements and neural networks that will extract the mapping between voice and movement features. In the first stage, where the prosody features and audio beat features extracted from music are used as music features, and the coordinates of key points of the human body extracted from dance videos are used as motion features for training. In the second stage, the basic mapping of music and dance movements are realized through the generator module of the model to generate a smooth dance posture; the consistency of dance and music is realized through the discriminator module; the audio characteristics are more possessed through the Autoencoder module representative. In the third and final stage, the modified version of the model transforms the dance posture sequence into a realistic version of the dance. Finally, a realistic version of the dance that fits the music is obtained. The experimental data is obtained from dance videos on the Internet, and the experimental results are analyzed from five aspects: loss function value, comparison of different baselines, evaluation of sequence generation effect, user research, and quality evaluation of real-life dance videos. The results show that the proposed dance generation model has a good effect in transforming into realistic dance videos.