Unsupervised learning of complex articulated kinematic structures combining motion and skeleton information

Hyung Jin Chang, Yiannis Demiris
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
more » ... dary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs stateof-the-art methods both quantitatively and qualitatively.
doi:10.1109/cvpr.2015.7298933 dblp:conf/cvpr/ChangD15 fatcat:bxk4leu3sje5pdr54p2446ez6q