Unsupervised hierarchical modeling of locomotion styles

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 stylespecific characteristics. We propose an algorithm that learns a hierarchical model of styles from unlabeled motion capture data by exploiting the cyclic property of human locomotion. We assume that sequences with the same style contain locomotion cycles generated by noisy, temporally warped versions of a single latent cycle. We model these style-specific latent cycles as random variables drawn from a common "parent" cycle distribution, representing the structure shared by all motions. Given these hierarchical priors, the algorithm learns, in a completely unsupervised fashion, temporally aligned latent cycle distributions, each modeling a specific locomotion ...

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Wei Pan, Lorenzo Torresani
• Keywords: Contain Locomotion Cycles, ICML 2009, Machine Learning, Specific Locomotion Style, Style-specific Latent Cycles