## 4 Experiments
### Comparison with Previous Methods
#### 4.3.3 Qualitative Results  
To better evaluate our method, we provide several qualitative visualization results. We randomly choose some action samples from testing set and prediction the corresponding future motion. Figure 4 illustrates the Ground Truth (GT) and Predicted (Pred) poses for multiple actions, including Posing, Walking, Waiting, and Purchases. The visualizations serve as Validation of Model Generalization: By comparing GT and Pred across diverse action types (periodic, static, and dynamic), we demonstrate the model's ability to handle varied motion patterns. Figure 5 visualizes suboptimal predictions (marked with red dashed boxes) alongside GT for multiple actions, including Posing, Greeting and Discussion. These visualizations are critical for identifying model limitations by highlighting scenarios where the model struggles (e.g., sudden pose changes, interactions with external objects) guides future improvements.  
Figure 6 visualizes the amplitude of joint torques estimated by the dynamic model, with blue dashed circles indicating joints or body parts exhibiting significant dynamic features. It can be observed that regions with noticeable changes in joint torque coincide with substantial limb movements, which suggests that the joint torques we have extracted are correlated with human motion and can be utilized to assist in motion prediction.  
To further demonstrate the extracted joint torque is correlated with human motion, we statistically analyze the correlation between the motion of upper limb joints and their corresponding torque. As shown in Figure 7, the left panel presents the distribution of  
Figure 5: Visualizations of some suboptimal Predictions (Pred) and Ground Truth (GT) in H3.6M. Note, less accurate predicted poses are marked with red dashed rectangles.  
Figure 6: Visualization joint torques of three actions: walking, sitting down, and walking dog. Note, the blue dashed circles indicate joints or body parts exhibiting significant dynamic features.  
Pearson correlation coefficients between joint velocity (which can be considered as a key indicator reflecting real-time limb movement) and corresponding joint torque magnitudes, with an average correlation coefficient of approximately 0.44 \(\pm\) 0.08. Although this value does not traditionally indicate a strong linear correlation, the distribution peak and its overall bias towards the positive correlation range suggest a certain consistency between torque variations and joint motion. Meanwhile, the boxplot and scatter distribution in the right panel demonstrate the repeatability of correlation coefficients across multiple samplings (IQR (interquartile range): 0.13-0.40). It can be observed that most samples' correlation coefficients cluster within the positive correlation range with relatively stable distribution, indicating a consistent positive correlation trend between torque and joint motion across different tests or data collection sessions.  
Based on these distribution patterns, we infer that significant changes in joint torque are often accompanied by noticeable alterations in corresponding joint motion (velocity). This synergistic relationship holds predictive implications for movement analysis. However, it is important to note that Pearson coefficients only measure linear correlations. Although the linear correlation here is not \"highly significant\", this does not exclude the possibility of more complex nonlinear relationships between joint torque and motion. For this reason, we employed artificial neural networks in subsequent research and experiments to capture potential nonlinear mapping relationships. The quantitative effects of joint torque in motion prediction and its contribution to improved prediction accuracy are discussed in greater detail in Section 4.4.1.