• ETH Zurich, Human Motion Analysis, Spring 2010

    This course will cover the theory and practice of human motion analysis using computer vision, machine learning and computer graphics techniques. We will expect students to have sufficient background to be able to read CVPR, NIPS and SIGGRAPH papers. We will review classic and contemporary methods for human motion classification, pose estimation and simulation. Representations of human motion, and classic human motion generation approaches including direct kinematics, inverse kinematics and motion grpahs will be reviewed at the beginning of the course. Discriminative approaches to tracking will be covered, including NN, regression techniques and Bayesian mixture of experts, as well as classic generative approaches to human body tracking such as the popular condensation algorithm, particle filters and likelihood models. Finally, we will review priors for human pose estimation and character animation including subspace models (e.g., PCA, GPLVM, Mixture of Factor Analyzers), joint limits and shape models. As time permits, we will cover related methods for gesture recognition based on human body motion, as well as physics-based approaches to tracking and character animation.