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Unsupervised motion capture data segmentation based on topic model
Author(s) -
Hu Xiaoyan,
Bao Xizhao,
Xie Shunbo,
Wei Guoli
Publication year - 2021
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.2005
Subject(s) - computer science , artificial intelligence , motion (physics) , segmentation , cluster analysis , computer vision , motion estimation , motion capture , pattern recognition (psychology) , silhouette
In this paper, we propose an unsupervised motion segmentation method based on topical model borrowed from Natural Language Processing. We apply hierarchical clustering on motion dataset to obtain a list of representative poses to constitute motion 'vocabulary'. By doing so, motion capture data can be viewed as text which comprises a sequence of motion words. We use sliding window to generate a sequence of motion documents (with overlap between consecutive motion documents). Then we use Sparse Topical Coding (STC) model to extract sparse topical codes of motion documents and conduct spectral clustering to get motion segmentations. Silhouette coefficient is used to determine the value of K (number of motion types). The results of experiments show that our method can segment motions with a very high accuracy. Our method has a strong generalization ability that also performs well on motion data which is captured by different subjects, with various motion types, even though they are from different motion dataset (HDM05 in our experiment).