Pictorial Structures for Object Recognition
International Journal of Computer Vision
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
We are family: joint pose estimation of multiple persons
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Parsing human motion with stretchable models
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Automatic recovery of 3d pose of multiple interacting subjects from unconstrained monocular image sequence is a challenging and largely unaddressed problem. We observe, however, that by tacking the interactions explicitly into account, treating individual subjects as mutual "context" for one another, performance on this challenging problem can be improved. Building on this observation, in this paper we develop an approach that first jointly estimates 2d poses of people using multi-person extension of the pictorial structures model and then lifts them to 3d. We illustrate effectiveness of our method on a new dataset of dancing couples and challenging videos from dance competitions.