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 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.

• Type: Conference
• Year: 2007
• Where: MLMI
• Authors: Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrence
• Keywords: Latent Space, Latent Variable Model, Machine Learning, MLMI 2007, Model