Learning Human Pose and Motion Models for Animation

author: Aaron Hertzmann, University of Toronto
published: Feb. 25, 2007,   recorded: June 2006,   views: 7468

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Computer animation is an extraordinarily labor-intensive process; obtaining high-quality motion models could make the process faster and easier. I will describe methods for learning models of human poses and motion from motion capture data. I will begin with a pose model based on the Gaussian Process Latent Variable Model (GPLVM), and the application of this model to Inverse Kinematics posing. I will then describe the Gaussian Process Dynamical Model (GPDM) for modeling motion dynamics. I may also mention a few other extensions to the GPLVM for modeling motion data. I will discuss the properties of these models (both good and bad) and potential directions for future work.

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