Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Mathieu Salzmann, Raquel Urtasun


Estimating 3D pose from monocular images is a highly ambiguous problem. Physical constraints can be exploited to restrict the space of feasible configurations. In this paper we propose an approach to constraining the prediction of a discriminative predictor. We first show that the mean prediction of a Gaussian process implicitly satisfies linear constraints if those constraints are satisfied by the training 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.