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. © 2012 Springer-Verlag.
CITATION STYLE
Li, H., Niu, J., Zhang, L., & Hu, B. (2012). Spatio-temporal LTSA and its application to motion decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7667 LNCS, pp. 498–505). https://doi.org/10.1007/978-3-642-34500-5_59
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