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» Gaussian Process Dynamical Models for Human Motion
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CVPR
2006
IEEE
14 years 6 months ago
3D People Tracking with Gaussian Process Dynamical Models
We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of hu...
Raquel Urtasun, David J. Fleet, Pascal Fua
PAMI
2008
182views more  PAMI 2008»
13 years 4 months ago
Gaussian Process Dynamical Models for Human Motion
We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motio...
Jack M. Wang, David J. Fleet, Aaron Hertzmann
CVPR
2009
IEEE
13 years 8 months ago
Switching Gaussian Process Dynamic Models for simultaneous composite motion tracking and recognition
Traditional dynamical systems used for motion tracking cannot effectively handle high dimensionality of the motion states and composite dynamics. In this paper, to address both is...
Jixu Chen, Minyoung Kim, Yu Wang, Qiang Ji
ICIP
2007
IEEE
14 years 6 months ago
Monocular Tracking 3D People By Gaussian Process Spatio-Temporal Variable Model
Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temp...
Junbiao Pang, Laiyun Qing, Qingming Huang, Shuqian...
ICCV
2007
IEEE
14 years 6 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...