A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is
Unsupervised learning of complex articulated kinematic structures combining motion and skeleton information
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 objectdoi:10.1109/cvpr.2015.7298933 dblp:conf/cvpr/ChangD15 fatcat:bxk4leu3sje5pdr54p2446ez6q