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.