Gaussian Process Latent Variable Models For Human Pose Estimation

Carl Henrik EkPhilip H. S. TorrNeil D. Lawrence
,  4892:132-143, 2008.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-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}, pages = {132--143}, year = {}, editor = {}, volume = {4892}, address = {Brno, Czech Republic}, 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.} }
Endnote
%0 Conference Paper %T Gaussian Process Latent Variable Models For Human Pose Estimation %A Carl Henrik Ek %A Philip H. S. Torr %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-ek-pose07 %I PMLR %J Proceedings of Machine Learning Research %P 132--143 %R 10.1007/978-3-540-78155-4_12 %U http://inverseprobability.com %V %W PMLR %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.
RIS
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 - PY - DA - ED - ID - pmlr-v-ek-pose07 PB - PMLR SP - 132 DP - PMLR EP - 143 DO - 10.1007/978-3-540-78155-4_12 L1 - 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 -
APA
Ek, C.H., Torr, P.H.S. & Lawrence, N.D.. (). Gaussian Process Latent Variable Models For Human Pose Estimation. , in PMLR :132-143

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