# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 4 Experiments
### Comparison with State-of-the-Art Methods
A visual representation of these outcomes is presented in Figure 8. Remarkably, the predictions made by SCGFormer closely align with the ground truth labels in these scenarios. These findings underscore the model's good learning capability and predictive performance. To assess the generalization prowess of our model, we trained it exclusively on the Human3.6M dataset [30; 31] and subsequently evaluated it on the test set of MPI-INF-3 DHP [12]. The outcomes are presented in Table 3. Impressively, our method outperforms the majority of other approaches, achieving an average PCK (percentage of correct keypoints) of 79.2 and an AUC (area under curve) of 43.9. These results underscore the robust generalization capabilities of our architecture, even when applied to previously unseen datasets. Figure 6: Bar chart of the overall average MPJPE (in mm) corresponding to the methods in Table 2. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline
**Protocol\#1** & **Direct.** & **Discuss** & **Eating** & **Greet** & **Phone** & **Photo** & **Pose** & **Purch.** & **Sitting** & **SittingD.** & **Smoke** & **Wait** & **WalkD.** & **Walk** & **WalkT.** & **Avg.** \\ \hline [PERSON] [31] (*) & 67.4 & 71.9 & 66.7 & 69.1 & 72.0 & 77.0 & 65.0 & 68.3 & 83.7 & 96.5 & 71.7 & 65.8 & 74.9 & 59.1 & 63.2 & 71.9 \\ [PERSON] [12] (*) & 52.6 & 64.1 & 55.2 & 62.2 & 71.6 & 79.5 & 52.8 & 68.6 & 91.8 & 118.4 & 65.7 & 63.5 & 49.4 & 76.4 & 53.5 & 68.6 \\ [PERSON] [37] & 51.8 & 56.2 & 58.1 & 59.0 & 69.5 & 78.4 & 55.2 & 58.1 & 74.0 & 94.6 & 62.3 & 59.1 & 65.1 & 49.5 & 52.4 & 62.9 \\ [PERSON] [15] (*) & 54.8 & 60.7 & 58.2 & 71.4 & 62.0 & 65.5 & 53.8 & 55.6 & 75.2 & 111.6 & 64.1 & 66.0 & 51.4 & 63.2 & 58.3 & 64.9 \\ [PERSON] [17] (*) & 54.2 & 61.4 & 60.2 & 61.2 & 79.4 & 78.3 & 63.1 & 81.6 & 70.1 & 107.3 & 69.3 & 70.3 & 74.3 & 51.8 & 63.2 & 69.7 \\ [PERSON] [18] (*) (*) & 52.8 & 54.8 & 54.2 & 54.3 & 61.8 & **53.1** & 53.6 & 71.7 & 86.7 & 61.5 & 67.2 & 53.4 & 47.1 & 61.6 & 53.4 & 59.1 \\ [PERSON] [57] & 50.1 & 54.3 & 57.0 & 57.1 & 66.6 & 73.3 & 53.4 & 55.7 & 72.8 & 88.6 & 60.3 & 57.7 & 62.7 & 47.5 & 50.6 & 50.4 \\ [PERSON] [14] (*) (*) & 51.5 & 58.9 & 50.4 & 57.0 & 62.1 & 65.4 & 49.8 & 52.7 & 69.2 & 85.2 & 57.4 & 58.4 & **43.6** & 60.1 & 47.7 & 58.6 \\ [PERSON] [88] & 48.4 & 50.7 & 57.2 & 55.2 & 63.1 & 72.6 & 53.0 & 51.7 & 66.1 & 80.9 & 59.0 & 57.3 & 62.4 & 46.6 & 49.6 & 58.3 \\ [PERSON] [51] (*) & 48.5 & 54.4 & 54.5 & 52.0 & 59.4 & 65.3 & 49.9 & 52.9 & 65.8 & 71.1 & 56.6 & 52.9 & 60.9 & 44.7 & 47.8 & 56.2 \\ [PERSON] [27] & 48.2 & 60.8 & 51.8 & 64.0 & 64.6 & 53.6 & 51.1 & 67.4 & 88.7 & **57.7** & 73.2 & 65.6 & 48.9 & 64.8 & 51.9 & 60.8 \\ [PERSON] [56] & 48.6 & 54.5 & 54.2 & 58.7 & 62.2 & 72.0 & 50.5 & 54.3 & 70.0 & 78.3 & 88.1 & 55.4 & 61.4 & 45.2 & 49.7 & 58.0 \\ C1 Class [25] (*) & 46.8 & 54.2 & **43.7** & 50.4 & 52.9 & 68.9 & 46.6 & 46.4 & 60.2 & 78.9 & 51.2 & 50.0 & 54.8 & 40.3 & 43.2 & 52.7 \\ [PERSON] [53] & 46.3 & 52.2 & 47.3 & 50.7 & 55.5 & 67.1 & 49.2 & **46.0** & 60.4 & 71.1 & 51.5 & 50.1 & 54.5 & 40.3 & 43.7 & 52.4 \\ [PERSON] [26] & 45.2 & 49.9 & 47.5 & 50.9 & 54.9 & 66.1 & 48.5 & 46.3 & 59.7 & 71.5 & 51.4 & 48.6 & 53.9 & 39.9 & 44.1 & 51.9 \\ [PERSON] [28] & 45.2 & 50.8 & 48.0 & 50.0 & 54.9 & 65.0 & **48.2** & 47.1 & 60.2 & 70.0 & 51.6 & 48.7 & 54.1 & 39.7 & 43.1 & 51.8 \\ Ours & **44.6** & **49.7** & 46.2 & **49.4** & **52.7** & 61.1 & 48.3 & 46.5 & **58.2** & 66.4 & **50.7** & **48.0** & 52.7 & **38.9** & **42.1** & **50.4** \\ \hline \end{tabular}
\end{table} 
Table 1: Results (m mm) of the quantitative evaluation using MPJPE on Human3.6M [30,31] according to protocol I. The notation (*) in the table indicates that the model uses the picture as input, while the rest use the 2D skeleton key points detected by the CPN network [21] as input, and the notation (*) indicates that the model was trained using additional data from MPH [59]. The best-performing data have been bolded. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline
**Protocol\#1** & **Direct.