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

Hyung Jin Chang, Yiannis Demiris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE Computer Society
Pages3138-3146
ISBN (Electronic)978-1-4673-6964-0
ISBN (Print)978-1-4673-6965-7
DOIs
Publication statusPublished - 7 Jun 2015
Event2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Boston, Ma, United States
Duration: 7 Jun 201512 Jun 2015

Conference

Conference2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Country/TerritoryUnited States
CityBoston, Ma
Period7/06/1512/06/15

Fingerprint

Dive into the research topics of 'Unsupervised learning of complex articulated kinematic structures combining motion and skeleton information'. Together they form a unique fingerprint.

Cite this