# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 4 Experiments
### Comparison with State-of-the-Art Methods  
In previous research, 3D human pose estimation methods have been categorized into two distinct groups based on their network inputs. The first category involves the direct extraction of essential feature information from images to predict joint points [12; 13; 14; 15; 17; 18; 55; 56]. The second category utilizes a well-established 2D pose detector to forecast crucial point information, after which the network deduces the 3D spatial positions of the joints [25; 26; 27; 28; 29; 37; 53; 57; 58]. The results comparing the model presented in this paper with previous works [12; 13; 14; 15; 18; 25; 26; 27; 28; 29; 37; 53; 55; 56; 57; 58] are summarized in Table 1.  
Among the aforementioned studies, some [12; 13; 14; 15; 18] have adopted an end-to-end approach that takes images as input. However, these approaches have yielded unsatisfactory estimation results primarily due to the limited feature information available in the images. When confronted with occlusion issues, these models, which rely solely on a single view, struggle to accurately infer hidden features. Furthermore, these models exhibit heightened sensitivity to various factors, such as background and lighting conditions, which contributes to reduced model robustness and generalization. In contrast, leveraging the two-dimensional skeleton provides access to a wealth of spatial kinematic information [28; 37], enabling the exclusion of extraneous image features during model training. As depicted in Table 1, all of these models utilize the output from the cascaded pyramid network (CPN) [21] as their input. Notably, among the 15 actions assessed in the Human3.6M [30; 31] dataset, our approach achieves the lowest overall prediction error for nine actions and excels in scenarios involving occluded frames, such as greeting, phoning, and sitting. The average error across all actions experiences a substantial reduction of nearly one and a half points compared to the most recent work [28]. The bar chart is shown in Figure 5. These results demonstrate the superior performance of our method in comparison to state-of-the-art techniques, with the exception of action poses. Furthermore, when compared to other methods [26; 27; 53] that employ graph convolution, our approach consistently delivers superior results for almost all actions. Figure 5: Bar chart of the overall average MPJPE (in mm) corresponding to the methods in Table 1.  
In Table 2, we conducted experiments using the ground truth (GT) values of the 2D key points. Our method outperforms others by achieving the best results for nine actions and yielding the lowest overall mean error. When compared to recent works utilizing graph convolution [26; 27; 28; 53], our model excels in handling accurate 2D information, demonstrating our significant advancements in processing 2D information with noise. The bar chart of average error is shown in Figure 6.  
We visualized the predictive performance of a majority of actions, as depicted in Figure 7, encompassing various movements such as posing (Pose), waiting (Wait), sitting down (SittingD), walking (Walk), and walking together (WalkT). The corresponding errors for these actions are tabulated in Table 1, measured in millimeters, as 48.3 for pose, 48.0 for wait, 66.4 for sitting, 38.9 for walking, and 42.1 for walking together. Among these actions, Wait, Walk, and WalkT demonstrate superior outcomes, with Pose achieving the second-best performance. For additional actions, including directions, walking dog (WalkD), phone usage (Phone), greeting (Greet), and sitting, the corresponding errors, reported in millimeters in Table 1, are 44.6, 52.7, 52.7, 49.4, and 58.2, respectively. In comparison to previous methodologies, Directions, Phone, Greet, and Sitting exhibit the best predictive performance, denoted by the lowest prediction errors within their respective action categories.