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SI3D
2006
ACM

Human motion estimation from a reduced marker set

10 years 22 days ago
Human motion estimation from a reduced marker set
Motion capture data from human subjects exhibits considerable redundancy. In this paper, we propose novel methods for exploiting this redundancy. In particular, we set out to find a subset of motion-capture markers that are able to provide fast and high-quality predictions of the remaining markers. We then develop a model that uses this reduced marker set to predict the others. We demonstrate that this subset of original markers is sufficient to capture subtle variations in human motion. We take a data-driven modeling approach to learn piecewise local linear models from a marker-based training set. We first divide motion sequences into segments of low dimensionality. We then retrieve a feature vector from each of the motion segments and use these feature vectors as modeling primitives to cluster the segments into a hierarchy of local linear models via a divisive clustering method. The selection of an appropriate linear model for reconstruction of a full-body pose is determined automat...
Guodong Liu, Jingdan Zhang, Wei Wang 0010, Leonard
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where SI3D
Authors Guodong Liu, Jingdan Zhang, Wei Wang 0010, Leonard McMillan
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