Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information

Hyung Jin Chang, Yiannis Demiris; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3138-3146

Abstract


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 boundary 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 state-of-the-art methods both quantitatively and qualitatively.

Related Material


[pdf]
[bibtex]
@InProceedings{Chang_2015_CVPR,
author = {Jin Chang, Hyung and Demiris, Yiannis},
title = {Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}