Human motion generation with multifactor models
Gengdai Liu, Mingliang Xu, Zhigeng Pan, Abdennour El Rhalibi
In Computer Animation and Virtual Worlds, 22(4), 2011.
Abstract: To generate human motions with various specific attributes is a difficult task because of high dimensionality and complexity of human motions. This paper presents a novel human motion model for generating and editing motions with multiple factors. A set of motions performed by several actors with various styles was captured for constructing a well-structured motion database. Subsequently, MICA (multilinear independent component analysis) model that combines ICA and conventional multilinear framework was adopted for the construction of a multifactor model. With this model, new motions can be synthesized by interpolation and through solving optimization problems for the specific factors. Our method offers a practical solution to edit stylistic human motions in a parametric space learnt with MICA model. We demonstrated the power of our method by generating and editing sideways stepping, reaching, and striding over obstructions using different actors with various styles. The experimental results show that our method can be used for interactive stylistic motion synthesis and editing.
Keyword(s): motion, multifactor, multilinear independent components analysis, tensor
@article{Liu:2011:HMG,
author = {Gengdai Liu and Mingliang Xu and Zhigeng Pan and Abdennour El Rhalibi},
title = {Human motion generation with multifactor models},
journal = {Computer Animation and Virtual Worlds},
volume = {22},
number = {4},
pages = {351--359},
year = {2011},
}
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