Neural networks based 3D posture reconstruction from orthogonal images of human performer
基于正交立体图像的3D人体姿态重建算法

A novel method for reconstructing 3D motion of human avatar from real-time orthogonal images by using artificial neural network techniques was proposed. The input vector to the network was constructed by using the extracted coordinates of the feature points, while the output one indicated the 3D coordinates of the representative points and joints of the real human. The fitting process of the neural network was based on some proper neural network learning techniques with a set of sample data pairs that were obtained by using a motion capture system ReActor. The proposed method was implemented on a personal computer and ran in real-time applications. And experimental results confirm both the feasibility and the effectiveness of the proposed method for estimating 3D human motion (reconstruction error in MSE is less than 5%).