Gaussian process latent variable models for human pose estimation
We describe a method for recovering 3D human body pose from silhouettes. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM) [1] encapsulating both pose and silhouette features Our method is generative, this allows us to model the ambiguities of a...
Κύριοι συγγραφείς: | Ek, CH, Torr, PHS, Lawrence, ND |
---|---|
Μορφή: | Conference item |
Γλώσσα: | English |
Έκδοση: |
Springer
2008
|
Παρόμοια τεκμήρια
-
Ambiguity modeling in latent spaces
ανά: Ek, CH, κ.ά.
Έκδοση: (2008) -
PoseField: an efficient mean-field based method for joint estimation of human pose, segmentation, and depth
ανά: Vineet, V, κ.ά.
Έκδοση: (2013) -
Surface Approximation by Means of Gaussian Process Latent Variable Models and Line Element Geometry
ανά: Ivan De Boi, κ.ά.
Έκδοση: (2023-01-01) -
Simultaneous segmentation and pose estimation of humans using dynamic graph cuts
ανά: Kohli, P, κ.ά.
Έκδοση: (2008) -
Discriminative Gaussian Process Latent Variable Model for Classification
ανά: Urtasun, Raquel, κ.ά.
Έκδοση: (2007)