# A Diffusion Modeling-Based System for Teaching Dance to Digital Human
## 2 Related Work  
In the context of research on music-driven digital human dance movement teaching systems, research work in related fields can be categorized as follows:  
1. Artificial intelligence in education In recent years, the application of artificial intelligence (AI) technology in education has been steadily increasing. [PERSON] et al. developed a student classroom behavior recognition system (WITS) utilizing convolutional neural networks (CNNs) and created a comprehensive student behavior dataset. Their system achieved an accuracy rate of 89.8% following extensive training [3]. [PERSON] explored the role of intelligent transformation in music education, examined its necessity, and proposed directions for developing an intelligent music teaching system [4]. The focus of educational AI research is to enhance the learning environment by analyzing and understanding students' learning activities. 2. Virtual digital human technology Virtual digital human technology has demonstrated significant potential across various domains. [PERSON] suggested that in the innovative development and effective preservation of intangible cultural heritage, the creation of a story system centered around virtual digital human characters represents a promising new direction [5]. In the entertainment sector, [PERSON] discussed the extensive use of virtual digital humans in films and games. By creating virtual idols through realistic digital human representations, this technology enhances user immersion and engagement [6]. 3. Music-driven dance generation Research on music-driven dance has evolved through three distinct stages. In the early stage, approaches focused on path matching, where choreography was developed based on the analysis of musical similarities. The middle stage introduced classical machine learning and deep learning techniques for music-based choreography. In the later stage, the field advanced with the application of generative artificial intelligence, incorporating multimodal and diffusion modeling methods, marking the onset of a new era in music-driven dance research. The concept of computer choreography was first proposed by [PERSON] in 1978, who generated dance poses based on movement probability. However, this approach lacked musical constraints, resulting in suboptimal outcomes [7]. In 2005, [PERSON] incorporated [PERSON]'s laws to aid computer choreography, which enabled the generation of several movements but with limited diversity [8]. In 2006, [PERSON] advanced the field by utilizing musical movement features to achieve synchronized dance movement synthesis. He extracted intensity features and beat changes from both movement and audio data, analyzed their relationship for dance movement synthesis, and compared and matched features between music and stored dance sequences to complete the choreography. An overview of the movement synthesis algorithm is illustrated in Figure 1 [9]. In 2016, [PERSON] and [PERSON] developed a Recurrent Neural Network (RNN) choreography system using deep learning techniques that could generate new dance sequences with some semantic coherence. However, their system did not account for musical factors [10]. [PERSON] et al. approached music-driven dance by symbolizing dance movements and reformulating the music-to-dance translation as an utterance retrieval problem. They introduced a semi-supervised learning method for music-driven dance, but the reduction of rhythmic information in the encoder input adversely affected the quality of the generated dance [11]. In 2022, [PERSON] et al. incorporated the diffusion model into human animation generation, achieving text-driven animation creation [12]. Building on this, [PERSON] et al. applied the diffusion model to music-driven dance, enabling the generation of dance movements from real audio inputs. Nonetheless, their approach requires further refinement in music feature encoding and dance generation [13].