Dance arrangement and style transfer methods driven by diffusion models and spatiotemporal graph convolutional networks

As the problems of traditional dance choreography methods, such as high dependence on manual experience and limited creation efficiency, have become increasingly prominent, this study is committed to exploring new methods driven by artificial intelligence (AI) technology to improve the degree of choreography automation and artistic diversity. This study proposes a multi-task learning framework based on diffusion models and spatio-temporal graph convolutional networks. Through the framework’s strong sequence modeling and style decoupling capabilities, two core tasks, dance motion generation and style transfer, are realized. The study adopts a Transformer-based music-motion cross-modal encoder to achieve semantic alignment of input music features. Then, the study uses a conditional denoising diffusion probabilistic model to generate basic dance motion sequences, and finally realizes accurate control and conversion of different dance styles through a style encoder and an adaptive instance normalization module. Experimental results on the AIST + + dataset show that: the proposed method achieves a Fréchet Initial Distance (FID) value of 5.23 in terms of dance motion generation quality, the Dynamic Time Warping (DTW) index for music synchronization is reduced to 5.11, and the subjective evaluation score reaches 4.33. All indicators are significantly better than those of existing baseline models. Ablation experiments confirm the core role of the adaptive instance normalization module in style control, and the cross-style FID value in the style transfer task is stably around 6.0. This study provides an effective technical path for solving the problems of automation and stylization in dance choreography. Its generated results show the potential to approach the level of professional choreography in terms of artistic expressiveness and technical indicators, which is of great value for promoting the application of AI in the field of creative content generation.