A dance movement quality evaluation model using transformer encoder and convolutional neural network.

The purpose of this study is to put forward a new evaluation model of dance movement quality to deal with the subjectivity and inconsistency in traditional evaluation methods. In view of the complexity and diversity of dance art and the widespread popularity of dance videos on social media, it is particularly urgent to develop an automatic and efficient tool for evaluating the quality of dance movements. Therefore, this study puts forward the Transformer Convolutional Neural Network with Dynamic and Static Streams (TransCNN-DSSS) model, which combines the analysis of dynamic flow and static flow, and makes use of the advantages of Transformer and Convolutional Neural Network (CNN) to deeply analyze and evaluate the dance movements.