Vector Space Modeling of Classical Chinese Dance Movements Based on Motion Capture Technology

Abstract

The development of motion capture technology provides the possibility of in-depth analysis of Chinese classical dance movements. In order to facilitate the measurement and calculation of classical dance movement data, this paper is divided into different human detection nodes. The TrignoIM wireless EMG wireless collector is selected to collect human electromyography data, and the data errors of the gyroscope, accelerometer, and magnetometer are analyzed and processed to construct the Chinese classical dance movement data set. Subsequently, Maya animation software was used to spatially model the character body and finger bone vectors of classical Chinese dance movements and combined with neural fusion shape techniques such as envelope deformation branching and residual deformation branching for bone building and skin weight binding. In order to test the effectiveness of the motion capture and modeling techniques in this paper, they are applied to the digital teaching of classical Chinese dance. The comparison group and the conventional group were selected to adopt different training methods, and a final assessment was conducted after completing the classical dance course. When it came to movement amplitude, the conventional teaching scored 4.06 points higher than the comparison group, and the comparison group performed better than the conventional group in all other areas. Classical Chinese dance movement vector space modeling based on motion capture technology is able to standardize classical dance movements.