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» Learning Articulated Structure and Motion
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ICCV
2007
IEEE
14 years 9 months ago
Real-time Body Tracking Using a Gaussian Process Latent Variable Model
In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is a...
Shaobo Hou, Aphrodite Galata, Fabrice Caillette, N...
ICML
2009
IEEE
14 years 8 months ago
Learning nonlinear dynamic models
We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise difficult. We provide theory sh...
John Langford, Ruslan Salakhutdinov, Tong Zhang
CVPR
2008
IEEE
14 years 9 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...
VRST
2005
ACM
14 years 1 months ago
Computing inverse kinematics with linear programming
Inverse Kinematics (IK) is a popular technique for synthesizing motions of virtual characters. In this paper, we propose a Linear Programming based IK solver (LPIK) for interactiv...
Edmond S. L. Ho, Taku Komura, Rynson W. H. Lau
HUMO
2007
Springer
13 years 11 months ago
Efficient Upper Body Pose Estimation from a Single Image or a Sequence
We propose a method to find candidate 2D articulated model configurations by searching for locally optimal configurations under a weak but computationally manageable fitness functi...
Matheen Siddiqui, Gérard G. Medioni