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Individual 3D Model Estimation for Realtime Human Motion Capture
Accepted to ICVRV, 2017.

Lianjun Liao, Le Su and Shihong Xia

Abstract
In this paper, we present a practicable method to estimate individual 3D human model in a low cost multiview realtime 3D human motion capture system. The key idea is: using human geometric model database and human motion database to establish human geometric priors and pose prior model; when given the geometric priors, pose prior and a standard template geometry model, the individual human body model and its embedded skeleton can be estimated from the captured 3D point cloud from multiple depth cameras. Because of the introduction of the global prior model of human body pose and shapes into a unified nonlinear optimization problem for human geometry model estimation, the accuracy of geometric model estimation is significantly improved. The experiments on the synthesized data set with noise or without noise and the real data set captured from multiple depth cameras show that estimation result of our method is more reasonable and accurate than the classical method, and our method is better noise-immunity. The proposed new individual 3D geometric model estimation method is suitable for online realtime human motion tracking system.
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Individual 3D Model Estimation for Realtime Human Motion Capture.  Accepted to ICVRV, 2017. [PDF 1,764KB]