Application of Deep Learning in the Restoration and Design of Traditional Dance Movements

The restoration and design of traditional dance movements pose intricate challenges due to the temporal complexity and cultural specificity of human motion. Conventional methods relying on manual notation or observation often lack precision and scalability. In this work, we present a deep learning framework that integrates hierarchical motion representation, rhythmic attention mechanisms, and transformer-based modeling to restore and synthesize traditional dance sequences. By combining 3D motion capture data with culturally specific datasets, the model effectively captures both spatial and temporal features of dance, ensuring stylistic fidelity and contextual relevance. The system employs graph convolutional networks to model joint dependencies, temporal convolution networks for sequential motion patterns, and a rhythm-aware attention module that aligns movement with music in real time. We incorporate explainable AI techniques to enhance model interpretability, ensuring the generated sequences remain consistent with cultural expectations. Compared to conventional and state-of-the-art approaches, our framework achieves improved performance in pose estimation and motion reconstruction, with experimental results demonstrating superior accuracy across Human3.6M, Penn Action, 3DPW, and MuPoTS-3D datasets. These findings underscore the advantages of our approach in maintaining authenticity while enabling innovation in traditional choreography. The proposed model provides a powerful and interpretable solution for digital preservation, automated analysis, and creative extension of traditional dance, offering new pathways for cultural heritage conservation and contemporary performance design in the age of intelligent media.