Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation
Mathieu Salzmann, Raquel Urtasun · 2010
Estimating 3D pose frommonocular images is a highly ambiguous problem. Phys-ical constraints can be exploited to restrict the space of feasible configurations. In this paper we propose an approach to constraining the prediction of a discrimi-native predictor. We first show that the mean prediction of a Gaussian process implicitly satisfies linear constraints if those constraints are satisfied by the train-ing examples. We then show how, by performing a change of variables, a GP can be forced to satisfy quadratic constraints. As evidenced by the experiments, our method outperforms state-of-the-art approaches on the tasks of rigid and non-rigid pose estimation. 1