Efficient multi-viewpoint acquisition of 3D objects undergoing repetitive motions
Proceedings of the 2007 symposium on Interactive 3D graphics and games
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Incremental discovery of object parts in video sequences
Computer Vision and Image Understanding
Learning to Look at Humans -- What Are the Parts of a Moving Body?
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Learning kinematic models for articulated objects
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning Articulated Structure and Motion
International Journal of Computer Vision
Real-time automatic kinematic model building for optical motion capture using a Markov random field
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Affine warp propagation for fast simultaneous modelling and tracking of articulated objects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Learning from demonstration in robots: Experimental comparison of neural architectures
Robotics and Computer-Integrated Manufacturing
A probabilistic framework for learning kinematic models of articulated objects
Journal of Artificial Intelligence Research
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Application of heterogenous motion models towards structure recovery from motion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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We investigate the problem of learning the structure of an articulated object, i.e. its kinematic chain, from feature trajectories under affine projections. We demonstrate this possibility by proposing an algorithm which first segments the trajectories by local sampling and spectral clustering, then builds the kinematic chain as a minimum spanning tree of a graph constructed from the segmented motion subspaces. We test our method in challenging data sets and demonstrate the ability to automatically build the kinematic chain of an articulated object from feature trajectories. The algorithm also works when there are multiple articulated objects in the scene. Furthermore, we take into account non-rigid articulated parts that exist in human motions. We believe this advance will have impact on articulated object tracking and dynamical structure from motion.