Spatio-temporal LTSA and its application to motion decomposition

Authors:
Hongyu Li;Junyu Niu;Lin Zhang;Bo Hu
Affiliations:
Electronic Engineering Department, Fudan University, Shanghai, China,School of Software Engineering, Tongji University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China;School of Software Engineering, Tongji University, Shanghai, China;Electronic Engineering Department, Fudan University, Shanghai, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Year:
2012

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Abstract

This paper describes a STLTSA-based framework to analyze and decompose human motion for synthesis. In this work, we mainly intend to extend a manifold learning method, local tangent space alignment, to a spatio---temporal version for manifold analysis and offer an effective method of estimating the intrinsic dimensionality of motion data. Based on an assumption that a long sequence of motion is composed of a number of short motion units, we can decompose a motion into several basic motion units in a low-dimensional manifold space and extract motion cycles from the cyclic unit. The generation of new complex movement using obtained motion units is feasible and promising.