# Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences
## 4 Conclusions  
In this paper, a 3D human pose recovery solution based on video sequences is proposed, and the SMPL model is used to model the 3D human body. Meanwhile, an end-to-end convolutional neural network is used to calculate the human-body pose parameters, shape parameters, and global camera parameters. In the process of 3D human pose perception, the first step is to pre-train the attitude-aware network based on a single image, using the 2D key-point dataset, 3D pose dataset, 3D human parameters dataset, and iPi dataset with human height information obtained by our motion capture experiment. The large-scale pre-training data ensures the network has a strong prediction and fitting ability. Meanwhile, the idea of GAN is introduced, which replaces the traditional prior rule constraints on the 3D human model, and simplifies the rule definition process, so that the generated 3D human model is as close as possible to the true structure distribution. In the second step, the video-based 3D human pose recovery method is further proposed. It is found that the feature space of deep convolutional neural networks can be directly optimized by integrating the correlation between video frames, the consistency of the initial state, and the human height constraint. Then, the feature space is decoded, so that the 3D human pose with higher precision is obtained by indirect calculation, and about 1000 picture sequences can be processed at a time. In the process of verifying the effectiveness of our proposed solution, we used two Kinect devices combined with the iPi Soft series software to build a motion capture platform and designed different motion capture experiments to compare and analyze the proposed solution with the self-built motion capture platform, at different speeds, different positions, different orientations, and complex actions. It is found that the inexpensive and convenient solution based on RGB video sequences proposed in this paper, combined with human height information, can obtain experimental results matching with commercial motion capture platform bases on RGB-D video sequences. The proposed method is beneficial to some key applications: The digital media industry (e.g., video games, animation, film), biomedical and health workplaces, and virtual human-machine ergonomics, which have very important, practical value.  
[PERSON] and [PERSON] supervised the work; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
This work was supported in part by the National Natural Science Foundation of China (grant #91748127) and National Key Research and Development Program of China (grant #2017 YFD0700102).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3D & three dimensional \\ SMPL & skinned multi-person linear \\ GAN & generative adversarial network \\ SGD & stochastic gradient descent \\ RMSE & root mean square error \\ MARG & magnetic, angular rate and gravity \\ LSP & Leeds Sports Poses \\ MPII & Motion Picture Industry Institute \\ MS COCO & Microsoft COCO \\ Human3.6M & 3.6 Million accurate 3D Human poses \\ MPI-INF-3 DHP & Max Planck Institute-Informatics-3D Human Pose \\ CMU & Carnegie Mellon University \\ BVH & Biovision Hierarchy \\ \end{tabular}