# Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences
## 2 Proposed Method
### 3D Human Pose Estimation Based on a Single Frame Image
#### 2.1.3 Model Pre-Training Process  
In the training of end-to-end networks, two data sources are adopted in the training process to get the parameterized SMPL model. One is the data source of the generated network, providing image data and label data for the network, and the other is the data source of the discriminant network, providing only the parameters of the SMPL model. 2D key-point coordinates with real marks are required to calculate the 2D projection error (\(L_{\textit{rep}ixel}\)), and the open-source Leeds Sports Poses (LSP) dataset, LSP-extended dataset [43], Motion Picture Industry Institute (MPII) dataset [44] and Microsoft COCO (MS COCO) dataset [45] are used. In calculating the error of 3D key points (\(L_{\textit{3d joints}}\)), 3D key-point coordinates with real marks are required. We use the open-source 3.6 Million accurate 3D Human poses (Human3.6M) dataset [46], the Max Planck Institute-Informatics-3D Human Pose (MPI-INF-3 DHP) dataset [47], and the iPi dataset obtained by the motion capture platform in this paper. In calculating the 3D parameter errors and SMPL real parameters of discriminant network, we use the Mosh method to obtain the SMPL parameters from real 3D key points, and calculate the loss of \(L_{\textit{3d smpl}}\) using only the Human3.6M dataset [46]. In training the depth discriminator network, we use datasets with SMPL  
Figure 3: The end-to-end 3D human-body pose estimation based on single-frame images. parameters including: the Human3.6M dataset, the Carnegie Mellon University (CMU) motion capture dataset, and the PosePrior dataset [48]. The datasets used for network training are summarized in Table 1. When calculating the 2D/3D key-point constraints, the inconsistency of the number of key points in the dataset will affect the calculation of 3D human key points. Therefore, the key points of all datasets are converted according to the order of the key points in the LSP dataset, and the key points that do not exist are treated as 0. For the MS COCO dataset, five key points of the face are added, and the numbers and names of the converted key points are shown in Figure 4.  
In the pre-training process, we use the Rodriguez formula to convert the pose parameters from the generated network \(\vec{\theta}\) into a \(3\times 3\) rotation matrix, which is used as the loss function and the input of the discriminant network. The learning rate of the generated network is set to 0.0001, the learning rate of the discriminant network is set to 0.00001, and the weight decay coefficient of the generated network and the discriminant network are both 0.0001. Using the skeleton point annotation of the image in each dataset, the matrix diagonal of the human body is scaled to 150 pixels, and the image is scaled to \(224\times 224\) size as the network input, while maintaining the aspect ratio of the image. In order to increase the diversity of training samples, random scaling, translation, and flip horizntals are used in the preprocessing of input images, in which the threshold for translation is set to [0, 20],