31 search results - page 2 / 7 » Gaussian Process Latent Variable Models for Human Pose Estim... |
PAMI
2008
13 years 4 months ago
2008
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...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We introduce models for density estimation with multiple, hidden, continuous factors. In particular, we propose a generalization of multilinear models using nonlinear basis functi...
ICCV
2007
IEEE
14 years 6 months ago
2007
IEEE
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...
ICASSP
2009
IEEE
13 years 11 months ago
2009
IEEE
This paper presents a multi-view articulated human motion tracking framework using particle filter with manifold learning through Gaussian process latent variable model. The dime...
DAGM
2010
Springer
13 years 6 months ago
2010
Springer
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...