# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 4 Experiments
### Ablation Experiments  
In this section, we conduct ablation experiments to investigate the impact of different graph convolution layers and determine the most optimal structure for the model. **Impact of Different Graph Convolutions:** [PERSON] et al. [28] initially employed a self-attention layer to capture global features, followed by the utilization of an LAM (learnable adjacency matrix) graph convolution layer to acquire information about adjacent  
Figure 8: Visualization results of actions such as Directions, WalkD, Phone, Greet, and Sitting. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Training Data**} & \multicolumn{4}{c}{**PCK**} & \multicolumn{1}{c}{**AUC**} \\ \cline{3-6}  & & **GS** & **noGS** & **Outdoor** & **Avg** & **All** \\ \hline [PERSON] [37] & H36M & 49.8 & 42.5 & 31.2 & 42.5 & 17.0 \\ [PERSON] [12] & H36M & 70.8 & 62.3 & 58.8 & 64.7 & 31.7 \\ [PERSON] [14] & H36M + MPII & - & - & - & 69.0 & 32.0 \\ [PERSON] [15] & H36M + MPII & 71.1 & 64.7 & 72.7 & 69.2 & 32.5 \\ [PERSON] [60] & H36M & 71.3 & 59.4 & 65.7 & 65.6 & 33.2 \\ [PERSON] [25] & H36M & 74.8 & 70.8 & 77.3 & 74.0 & 36.7 \\ [PERSON] [61] & H36M + MPII & 75.6 & 71.3 & **80.3** & 75.3 & 38.0 \\ [PERSON] [26] & H36M & **81.5** & **81.7** & 75.2 & **80.1** & **45.8** \\ [PERSON] [28] & H36M & 80.1 & 77.9 & 74.1 & 79.0 & 43.8 \\ ours & H36M & 80.3 & 77.6 & 74.0 & 79.2 & 43.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on MPI-INF-3 DHP [12] test set, and the best-performing data have been bolded. Figure 7: Visualization results of actions such as Pose, Wait, SittingD, Walk, and WalkT. points. They subsequently applied a Chebyshev graph convolution layer to learn implicit higher-order connectivity relationships between non-adjacent joints. While the LAM graph convolution enables the network to learn valuable information between neighboring nodes using the Laplacian operator, it may introduce instability during the learning process, potentially disrupting human kinematic structural constraints and weakening local connections between neighboring nodes. In this study, we replace the LAM graph convolution with semantic graph convolution (SemGconv) after extracting global features with self-attention. The semantic graph convolution layer provides stability to the results by using a fixed adjacency matrix. The visualization of the adjacency matrices used for semantic graph convolution and LAM graph convolution is presented in Figures 9 and 10, respectively. Brighter matrices indicate stronger associations between two nodes. In LAM, the network learns the adjacency matrix, as shown in Figure 9, capturing more distant neighborhood relationships. However, this fragile relationship comes at the cost of compromising the fundamental human kinematic structure. Semantic graph convolution employs a fixed adjacency matrix to learn node features after global feature extraction, reinforcing local adjacency relationships based on fixed human structural constraints. While this increases the number of parameters, it provides a more reasonable basis for the subsequent Chebyshev graph convolution to learn distant neighborhood relationships between nodes. We introduced modifications to the adjacency matrix, which serves as a prior constraint for Chebyshev graph convolution, by incorporating second-order adjacency information. Ablation experiments revealed that replacing the original Chebyshev graph convolution with this new version alone did not lead to improvements in test results or affect the performance of the original model. However, when combined with the improved Chebyshev graph convolution and semantic graph convolution, the prediction error decreased once again. This observation suggests that the LAM (learnable adjacency matrix) graph convolution may compromise body structure information and impact subsequent feature extraction. The experimental results, as presented in Table 4, utilize the 2D node information provided by the CPN (cascaded pyramid network) [21].