## 4 Results and Discussion
### Comparative Experiment Analysis  
This section evaluates the proposed method using the Human3.6M motion capture dataset. Similar to VideoPose, this section also uses the MPJPE indicator for evaluation. For Human3.6M, the network structure parameters are as follows: The number of input joint points N is 17. The length, T, of the 2D joint-point sequence is 243. The main network consists of four convolutional blocks, the first two being convolutional blocks A and the last two being convolutional blocks B. The convolutional kernel size and hole convolution scale are the same as those in the examples in Figures 3-6. In the local convolution, the number of output channels for each group of convolutions was 64. In global convolution, the number of output channels was 1088. The training parameters were as follows: the batch size was 256, the ranger was used as the optimizer, the initial learning rate was 0.001, and the learning rate of each iteration decayed by 95%. Eighty iterations were performed.  
In the case of using 2D annotations as the network input, this chapter compares it with previous studies on MPJPE metrics, the results of which are shown in Figure 9. The algorithm proposed in this chapter outperforms previous algorithms in predicting all actions, especially when compared with the baseline VideoPose method in this framework. The results showed a significant improvement, with an average error reduction of 4.8 mm. The experimental results show that the method proposed in this section has significant advantages and significantly improves the accuracy of 3D human pose estimation.  
To verify the robustness of this framework, we used the cascaded pyramid network (CPN) prediction results as inputs to the network. [PERSON] et al. (2018) proposed CPN to facilitate the accurate location of occluded and invisible points, utilizing multi-level features to clearly solve the detection problem of \"difficult points.\" Compared with pure 2D annotation, the predicted results of the CPN are not accurate and contain certain noise, which impacts the subsequent 3D human pose estimation. However, in a horizontal comparison, the algorithm proposed in this section still has significant advantages over the previous methods ([PERSON] et al., 2018). With the MPJPE index, the average error was reduced to 44.8 mm, whereas with the P-MPJPE index, it was reduced to 35.2 mm. Compared to the baseline method, the two indicators decreased by 2.0 mm (4.4%) and 2.6 mm (6.9%), respectively (Figure 10).  
The P-MPJPE index shows a greater decrease than the MPJPE index, which is nearly twice as large, indicating that this framework has a stronger regression ability in terms of action accuracy without considering scaling and rotation (Figure 11).  
Figure 8: Back pose estimation on the CMU Panoptic dataset for multi-views.  
Figure 11: Comparison of P-MPJPE indicators (mm) for 15 actions on the Human3.6M dataset (CPN prediction results as input), where methods refer to [PERSON] et al. [37], VideoPose [38], and [PERSON] et al. [39], respectively. The description of the Y axis is accuracy rate %.  
Figure 10: Comparison of MPJPE metrics (mm) for 15 actions on the Human3.6M dataset (CPN prediction results as input), where methods refer to [PERSON] et al. [37], VideoPose [38], and [PERSON] et al. [39], respectively. The description of the Y axis is accuracy rate %.  
Figure 9: Comparison of MPJPE metrics (mm) for 15 actions on the Human3.6M dataset (2D annotations as input), where the methods refer to [PERSON] et al. [37], VideoPose [37; 38], and [PERSON] et al. [39], respectively. The description of the Y axis is accuracy rate %.