# A Diffusion Modeling-Based System for Teaching Dance to Digital Human
## 3 Theoretical Framework of Digital Human Dance Teaching System
### Construction of Music-Driven Generative Dance Models  
The core idea of this system is to generate dance movement sequences based on user input of various music types. This involves using diffusion modeling to iteratively refine the initial dance sequences through multiple iterations. The final result is a dance movement demonstration performed by a digital human. The model is illustrated in Figure 7.  
The model network is divided into two main components: the cross-modal matching module and the dance sequence diffusion module. The network simulates the diffusion process by iteratively denoising dance sequences from time step T to time step 0. At each denoising step T, the cross-modal matching module performs a sample prediction of the dance sequences. Subsequently, the diffusion network denoises the dance sequences for the next time step. This process continues iteratively until the final dance sequences are obtained at time step 0.  
Figure 6: Transformer model structure.  
In the network model, the ADMGD-MD module realizes the prediction of noise to dance sequences, learns the parameters in the forward process of the diffusion model, and obtains the dance sequences at the moment of T in the inverse process, so that the diffusion model can complete the prediction of music-driven dance sequences [18]. Therefore, the use of this module to solve the problem of insufficient musical feature encoding capability of the old model is not coordinated with the generation of dance sequences; the specific internal structure is shown in Figure 8 below.  
Figure 8: ADMGD-MD Module Structure Diagram.  
Figure 7: Music-Driven Dance Generation Modeling Diagram.  
For the evaluation of the model, the system uses the following technical indicators:  
In terms of kinematic quality, to assess the kinematic quality of the generated dance sequences, we followed the previous method to calculate the Frechet Inception Distance (FID) distance between the kinematic features of the generated dances and the real dance sequences. The kinematic features (subscript \"k\") represent the velocity and acceleration of the movement and reflect the physical characteristics of the dance. Therefore, the FID distance FIDk between kinematic features measures the physical reality of the movement. Geometric features (subscript \"g\") are computed based on multiple predefined movement templates; therefore, the FID distance FIDg between geometric features reflects the quality of the overall choreography.  
In terms of movement diversity, in order to evaluate the movement diversity of the generated dance sequences, we compute the average Euclidean distance within the kinematic feature space, in agreement with [PERSON]. DIVk denotes the movement diversity in the kinematic feature space, while DIVg denotes the diversity in the geometric feature space.  
For the beat alignment scores, to evaluate the beat consistency between the generated dances and the given music, we followed [PERSON] and used BAS to evaluate our method.  
The dance sequences in this system follow the skinned multiplayer linear model data format, which is abbreviated as SMPL [19]. SMPL better solves the historical problem of skin collapse and wrinkles that can occur in linear hybrid skinning algorithms, and is suitable for the field of animation, which can be deformed naturally with pose changes and accompanied by natural movements of soft tissues. Therefore, it can better fit the shape of the human body and the deformation under different poses, and the shape and pose of the human body model can be parametrically represented, as shown in Figure 9 below.