Gaussian Process Latent Variable Models For Human Pose Estimation

[edit]

Carl Henrik Ek, University of Bristol
Philip H. S. Torr, University of Oxford
Neil D. Lawrence, University of Sheffield

in Machine Learning for Multimodal Interaction (MLMI 2007) 4892, pp 132-143

Related Material

Abstract

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 silhouette representation in a principled way. We learn a dynamical model over the latent space which allows us to disambiguate between ambiguous silhouettes by temporal consistency. The model has only two free parameters and has several advantages over both regression approaches and other generative methods. In addition to the application shown in this paper the suggested model is easily extended to multiple observation spaces without constraints on type.


@InProceedings{ek-pose07,
  title = 	 {Gaussian Process Latent Variable Models For Human Pose Estimation},
  author = 	 {Carl Henrik Ek and Philip H. S. Torr and Neil D. Lawrence},
  booktitle = 	 {Machine Learning for Multimodal Interaction (MLMI 2007)},
  pages = 	 {132},
  year = 	 {2008},
  editor = 	 {Andrei Popescu-Belis and Steve Renals and Hervé Bourlard},
  volume = 	 {4892},
  series = 	 {LNCS},
  address = 	 {Brno, Czech Republic},
  month = 	 {00},
  publisher = 	 {Springer-Verlag},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2008-01-01-ek-pose07.md},
  url =  	 {http://inverseprobability.com/publications/ek-pose07.html},
  abstract = 	 {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 silhouette representation in a principled way. We learn a dynamical model over the latent space which allows us to disambiguate between ambiguous silhouettes by temporal consistency. The model has only two free parameters and has several advantages over both regression approaches and other generative methods. In addition to the application shown in this paper the suggested model is easily extended to multiple observation spaces without constraints on type.},
  key = 	 {Ek:pose07},
  doi = 	 {10.1007/978-3-540-78155-4_12},
  linkpdf = 	 {ftp://ftp.dcs.shef.ac.uk/home/neil/mlmi.pdf},
  linksoftware = {https://github.com/SheffieldML/SGPLVM/},
  group = 	 {gplvm,pose estimation}
 

}
%T Gaussian Process Latent Variable Models For Human Pose Estimation
%A Carl Henrik Ek and Philip H. S. Torr and Neil D. Lawrence
%B 
%C Machine Learning for Multimodal Interaction (MLMI 2007)
%D 
%E Andrei Popescu-Belis and Steve Renals and Hervé Bourlard
%F ek-pose07
%I Springer-Verlag	
%P 132--143
%R 10.1007/978-3-540-78155-4_12
%U http://inverseprobability.com/publications/ek-pose07.html
%V 4892
%X 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 silhouette representation in a principled way. We learn a dynamical model over the latent space which allows us to disambiguate between ambiguous silhouettes by temporal consistency. The model has only two free parameters and has several advantages over both regression approaches and other generative methods. In addition to the application shown in this paper the suggested model is easily extended to multiple observation spaces without constraints on type.
TY  - CPAPER
TI  - Gaussian Process Latent Variable Models For Human Pose Estimation
AU  - Carl Henrik Ek
AU  - Philip H. S. Torr
AU  - Neil D. Lawrence
BT  - Machine Learning for Multimodal Interaction (MLMI 2007)
PY  - 2008/01/01
DA  - 2008/01/01
ED  - Andrei Popescu-Belis
ED  - Steve Renals
ED  - Hervé Bourlard	
ID  - ek-pose07
PB  - Springer-Verlag	
SP  - 132
EP  - 143
DO  - 10.1007/978-3-540-78155-4_12
L1  - ftp://ftp.dcs.shef.ac.uk/home/neil/mlmi.pdf
UR  - http://inverseprobability.com/publications/ek-pose07.html
AB  - 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 silhouette representation in a principled way. We learn a dynamical model over the latent space which allows us to disambiguate between ambiguous silhouettes by temporal consistency. The model has only two free parameters and has several advantages over both regression approaches and other generative methods. In addition to the application shown in this paper the suggested model is easily extended to multiple observation spaces without constraints on type.
ER  -

Ek, C.H., Torr, P.H.S. & Lawrence, N.D.. (2008). Gaussian Process Latent Variable Models For Human Pose Estimation. Machine Learning for Multimodal Interaction (MLMI 2007) 4892:132-143