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. 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. The end-to-end 3D human-body pose estimation based on single-frame images.

Experimental results show that our low-cost method based on RGB video data can achieve similar results to commercial motion capture platform with RGB-D video data.

Then we defined several different tasks, including the speed of the movements, the position of the subject, the orientation of the subject, and the complexity of the movements. To make a contrast, we first built a motion capture platform through two Kinect (V2) devices and iPi Soft series software to obtain depth-camera video sequences and monocular-camera video sequences respectively. In addition, we compared the proposed 3D human pose recovery method with the commercial motion capture platform to prove the effectiveness of the proposed method. Combined with the correlation of video sequences, a 3D human pose recovery method based on video streams is proposed, which uses the correlation between videos to generate a smoother 3D pose. The model is pre-trained using open-source human pose datasets (2) Human-body pose generation based on video streams. We use 2D/3D skeleton point constraints, human height constraints, and generative adversarial network constraints to obtain a more accurate human-body model. The method is divided into two main stages: (1) 3D human pose estimation based on a single frame image. This paper proposes a novel approach to estimate 3D human action via end-to-end learning of deep convolutional neural network to calculate the parameters of the parameterized skinned multi-person linear model. Using video sequences to restore 3D human poses is of great significance in the field of motion capture.

Ipi mocap studio 2.3.1 dual kinect