# A Diffusion Modeling-Based System for Teaching Dance to Digital Human
## 5 Conclusions  
The integration of digital media, virtual digital humans, and other emerging technologies into dance education has introduced new possibilities and development opportunities for traditional teaching methods [21]. Dance, a discipline that combines physical education with artistic expression, places significant demands on students' physical coordination, sense of rhythm, and expressiveness.  
The system demonstrates innovation in several key areas. Traditional models typically employ simple convolutional or Transformer networks for music feature encoding. By incorporating frequency domain processing and attention mechanisms, this system significantly enhances the accuracy and effectiveness of music feature encoding. Additionally, the dance sequence encoding enhancement module improves the quality and consistency of the generated dance movements, addressing common issues such as incoherence and unnatural defects prevalent in traditional models. The innovations of the system are as follows:  
1. Music-driven dance generation based on diffusion model implementation  
The system employs diffusion modeling in conjunction with other artificial intelligence techniques to develop a network model for music-driven dance generation. A music feature encoding module is introduced to address the limitations of previous models, thereby enhancing the alignment between music features and dance movements.
2. Improved Dance Sequence Encoding  
Following the development of an initial music-driven dance generation model, a dance sequence feature encoding module is introduced to address the shortcomings of previous models in dance sequence coding quality. This enhancement improves the coherence and naturalness of the generated dance movements.
3. Teaching Dance + Digital Human  
Compared to traditional dance teaching videos, the Digital Human Dance Movement Teaching System significantly enhances the precision and detail of movement simulation. Additionally, the system's intelligent algorithm can adapt dance movements to various styles and learning needs, thereby improving the system's applicability and scalability.  
The Diffusion Model-based digital human dance movement teaching system presented in this paper demonstrates significant advantages in naturalness, fluency, and overall teaching effectiveness. It shows substantial potential for advancing dance education and virtual digital human applications, offering new opportunities and advancements in educational technology. However, the system currently relies on static snapshots and animated video displays. Future research should focus on further optimizing the model to enhance the accuracy and diversity of dance movement generation and explore its applicability across a broader range of scenarios.  
[PERSON]: Responsible for writing the original draft of the manuscript, including the development and articulation of the core ideas and content, and drafted the manuscript; Contributed to the design of the study; assisted in the development of the system; conducted experiments; and revised the manuscript critically for important intellectual content. [PERSON]: Managed the validation of the diffusion modeling-based system, ensuring the accuracy and reliability of the results and methods. [PERSON]: Contributed to the review and editing of the manuscript, providing critical revisions and feedback to enhance the clarity and quality of the final submission. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Metauniverse and artificial intelligence research and development fund, China (No: YYK202301).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.