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Unsupervised learning of complex articulated kinematic structures combining motion and skeleton information
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object
doi:10.1109/cvpr.2015.7298933
dblp:conf/cvpr/ChangD15
fatcat:bxk4leu3sje5pdr54p2446ez6q