# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 2 Related Work
### 3D Human Pose Estimation  
Three-dimensional (3D) human pose estimation represents an important research area within computer vision [32, 33]. Its fundamental aim is to ascertain the spatial coordinates of human joints in three-dimensional space, primarily utilizing RGB images or video data. In its early stages, this task [34, 35, 36, 31] heavily relied on handcrafted features and geometric constraints as the means to predict 3D human pose. Nevertheless, with the rapid evolution of deep learning, deep neural networks have emerged as a predominant approach for 3D human skeleton prediction. These approaches can be broadly categorized into two primary types: single-stage regression and multi-stage methods.  
Single-stage regression stands as a direct approach to the end-to-end estimation of 3D human pose from images, characterized by its simplicity and effectiveness. For instance, [PERSON] et al. [13] employed voxel likelihood to represent the confidence of joint positions in 3D space and inferred joint details via 3D heat maps. Nevertheless, their model exhibits sensitivity to less pertinent factors, such as background and lighting conditions, and its generalization is impeded by a restricted volume of indoor data. In a different vein, [PERSON] et al. [15] adopted a weakly supervised training model that combined annotated 2D outdoor images with annotated 3D indoor images as inputs, effectively mitigating the prediction ambiguity from 2D to 3D. Additionally, [PERSON] et al. [14] employed an adversarial network featuring a multi-source discriminator to compel the generator to produce plausible 3D human poses.  
In contrast, multi-stage methods first employ CNN networks to detect 2D joint coordinates, followed by the utilization of this information in a 3D information detector to infer 3D human pose. These approaches heavily rely on a mature 2D pose detector [23; 24] and primarily focus on the transition from 2D to 3D, a strategy that often yields heightened data accuracy. For instance, [PERSON] et al. [37] introduced a straightforward yet highly effective 3D pose detector that achieves precise predictions through a simplistic network structure. In another vein, [PERSON] et al. [38] proposed a generative adversarial training approach to address the shortage of labeled 3D datasets. They employed two parallel networks to simultaneously predict 3D human pose and camera parameters, harnessing a discriminator to enhance pose generation accuracy. Additionally, they re-projected generated 3D poses back to 2D poses, thereby enriching the training data.  
The characteristics of 2D joint data differ from image data. While it avoids interference from factors such as background lighting in images, the compressed and concise feature information in 2D joint data necessitates an appropriate prior constraint for further discerning useful information between joints. Moreover, dealing with the continuity of a sequence of movements requires addressing the connections between joint data frames, emphasizing the significance of enlarging the receptive field. Disregarding these issues may compromise the accuracy of 3D human pose estimation. Therefore, effectively leveraging 2D joint data in multi-stage regression tasks becomes crucial. Based on this, our approach adopts a multi-stage regression method that integrates both graph convolution and Transformer techniques. In the subsequent sections, we will introduce recent research endeavors pertaining to human pose prediction through the utilization of graph convolution and Transformer.