Research on deep generation model for automatic dance movement generation and style conversion

This study is devoted to exploring the technology of automatically generating dance movements and style conversion, aiming at catering to the diversity of needs in dance creation, education and entertainment. Therefore, a new depth generation model is developed. The model integrates two modules: action generation and style conversion. In the aspect of action generation, the model uses VAE (Variational Autoencoders) to capture the temporal and spatial characteristics of dance actions, and then creates novel and diverse dance action sequences. In the style conversion module, this paper adopts the style transfer technology to integrate the characteristics of a dance style into the generated dance movements to realize the accurate conversion between different styles. The experimental results show that the model is excellent in generating dance movements, and the movements are smooth and colorful. It has also achieved remarkable results in style transformation. The transformed movements are highly compatible with the target style, which has won wide praise from dance professionals and users. The depth generation model proposed in this study has significant application value and academic contribution in automatic generation of dance movements and style transformation. It provides an innovative solution for the generation and transformation of dance movements.