# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 4 Experiments
### Experimental Details  
The dataset, evaluation metrics, and training details used in this work are described below. **Dataset:** We use two popular human pose datasets, Human3.6M [30, 31] and MPI-INF-3 DHP [12], to evaluate the SGraFormer. **Human3.6M**[30, 31] is the most widely used dataset for 3D human pose estimation tasks [28, 30, 52]. It encompasses a diverse set of 15 actions, ranging from common activities such as greeting and smoking to walking a dog, all enacted by a total of 11 different actors, captured across four cameras. Within the dataset, subjects denoted as S1, S5, S6, S7, and S8, sourced from seven actors, serve as the training and validation sets. Conversely, subjects S9 and S11, corresponding to the remaining four actors, are exclusively designated for testing purposes. In aggregate, the dataset comprises 1,559,752 frames for training and 543,344 frames for testing. **MPI-INF-3 DHP**[12] presents images captured in three distinct scenarios: a studio environment with a green screen (GS), a studio environment without a green screen (noGS), and an outdoor setting (Outdoor). We have employed this dataset to assess the generalization capabilities of the proposed architecture. **Evaluation metrics**: In the case of Human3.6M [30, 31], two distinct evaluation protocols, **Protocol 1** and **Protocol 2**, have been established, delineating different approaches based on subject selection within the dataset [26, 27, 37]. **Protocol 1** involves the utilization of subjects S1, S5, S6, S7, and S8 as the training set, with subjects S9 and S11 designated for testing. Conversely, **Protocol 2** employs subjects S1, S5, S6, S7, S8, and S9 for training, reserving S11 as the sole component of the testing set. In accordance with recent research practices [26, 27, 28, 37, 52, 53], we have adhered to the first evaluation protocol. Regarding the MPI-INF-3 DHP dataset [12], we have adhered to established conventions [26, 28] by employing 3D-PCK (percentage of correct keypoints) and AUC (area under the curve) as the chosen evaluation metrics. **Protocol 1** uses the mean error per joint position (MPJPE) \(E_{MPJPE}(f,S)\) as the evaluation metric, as shown in Equation (9). Given skeleton \(S\) with \(N_{S}\) joints, it first aligns the root joint (the pelvic joint), then calculates the mean error of the joint position. The smaller the value, the more accurate the prediction. \[E_{MPJPE}(f,S)=\frac{1}{N_{S}}\sum_{i=1}^{N_{S}}\left\|m_{pe,S}^{(f)}(i)-m_{gt,S}^{(f)}(i)\right\|_{2}, \tag{9}\]  
where \(m_{pe,S}^{(f)}(i)\) is a function that returns the coordinates of the i-th joint of skeleton \(S\), at frame \(f\), from the pose estimator \(pe\), and \(m_{gt,S}^{(f)}(i)\) is the i-th joint of the ground truth frame \(f\). **Protocol 2** employs P-MPJPE (Procrustes mean per joint position error) as the evaluation metric, wherein the predicted values are aligned with the ground truth through rigid transformations, encompassing translation and rotation. Subsequently, MPJPE is computed to quantify the error, with millimeters serving as the unit of measurement. It is noteworthy that the performance outcomes under both protocols typically exhibit consistency. Therefore, in alignment with established practices in the literature [27, 28], we have chosen to utilize **Protocol 1**, specifically the mean error per joint position (MPJPE), as the designated evaluation metric for our experiments. **Training parameter settings:** During the training phase, we configured the SCGFormer model with a total of five SGraAttention modules (as denoted by N in Figure 1), and each multi-headed self-attention utilized four heads (H). The intermediate feature dimension for data processing within the model was set to 96, accompanied by a dropout rate of 0.25. For optimization during training, we employed the classical Adam [54] optimizer, commencing with an initial learning rate of 0.001. The training data was organized into batches of size 64, and a learning rate decay rate of 0.9 was applied.