# Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences
## 1 Introduction  
Currently, in the digital video industry, motion capture methods based on high-speed cameras and key-point markers on the body surface are widely used [1]. However, this approach has the following disadvantages: (1) The markers on the human-body surface limits the freedom of human-body movement; (2) A controlled and restricted site is needed to obtain high-accuracy data; (3) Calculation is very time consuming, etc. On the other hand, the unmarked motion capture method based on ordinary optical cameras is convenient and inexpensive. In this method, the accuracy of motion capture is low and cannot meet some fields with high precision requirements, such as medical rehabilitation [2]. However, the precision requirements of the digital game industry and virtual human-machine ergonomics are not very strict, the rapid development of unmarked motion capture devices based on optical systems has been greatly promoted, and such devices have the benefits of acceptable computing time and convenient use.  
The existing mainstream three-dimensional (3D) pose perception method consists of two parts: multi-camera [3; 4; 5] and monocular camera [6; 7; 8]. The solution of restoring 3D human poses frommonocular images is arbitrary and uncertain, and most methods can only obtain the 3D skeleton points of the human body, but cannot directly obtain the motion information such as the rotation matrix between the nodes [9; 10], which needs to be solved by assumptions and inverse kinematics. To achieve the aforementioned goal, this paper proposes an end-to-end solution combined with a deep convolutional neural network, which can restore a 3D human grid model from single image data, and then smooth the serialized 3D human model using the correlation between RGB video frames and obtain the final human grid model. The solution proposed is to calculate the parameters of the parameterized skinned multi-person linear (SMPL) model [11] to restore the 3D pose of the human body. To constrain the arbitrariness in the solution process: On the one hand, we use 2D/3D skeleton point constraints to obtain a more accurate human-body model and combine human height constraints to reduce the arbitrariness problem in the two-dimensional (2D) to 3D solution process. On the other hand, we introduce the idea of a generative adversarial network (GAN) [12] and use the GAN constraint to generate a more human-like 3D human model. To evaluate the effectiveness of the motion capture method based on 3D human pose recovery, as proposed in this paper, we obtained the monocular-camera video sequences and depth-camera video sequences from the motion capture platform consisting of two Kinect (V2) devices and iPi Soft commercial software. Then we compared the performance of the proposed solution and this commercial motion capture platform in four different tasks for the variations of speed, position, orientation, and complex tasks. Experimental results show that the system performance of our low-cost method based on RGB (monocular-camera) video data is comparable to that of the commercial motion capture platform with RGB-D (depth-camera) video data.