Spatial constraints-based maximum likelihood estimation for human motions

Abstract

A new method of spatial constraints-based maximum likelihood estimation (SC-based MLE) is proposed to process latent variables data of incomplete human motions cycle, and improve the GPDM learning and estimation in this paper, which can make the GPDM learn the samples of incomplete human motions cycle to estimate the new human motions. The proposed method has the GPDM learning less depend on training samples of the complete human motions cycle, and save the training samples. We verify the validity and efficiency of the proposed method, through the experiments of human motions estimation using the samples of incomplete and complete human motions cycle for training respectively.