Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation
Abstract
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.
Cite
Text
Salzmann and Urtasun. "Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation." Neural Information Processing Systems, 2010.Markdown
[Salzmann and Urtasun. "Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation." Neural Information Processing Systems, 2010.](https://mlanthology.org/neurips/2010/salzmann2010neurips-implicitly/)BibTeX
@inproceedings{salzmann2010neurips-implicitly,
title = {{Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation}},
author = {Salzmann, Mathieu and Urtasun, Raquel},
booktitle = {Neural Information Processing Systems},
year = {2010},
pages = {2065-2073},
url = {https://mlanthology.org/neurips/2010/salzmann2010neurips-implicitly/}
}