Unsupervised hierarchical modeling of locomotion styles

  title={Unsupervised hierarchical modeling of locomotion styles},
  author={Wei Pan and Lorenzo Torresani},
This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the same motion performed by different subjects. Modeling motion styles requires identifying the common structure in the motions and detecting style-specific characteristics. We propose an algorithm that learns a hierarchical model of styles from unlabeled motion capture data by exploiting the cyclic property of human… CONTINUE READING
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