In multi-person pose estimation actors can be heavily occluded, even become fully invisible behind another person. While temporal methods can still predict a reasonable estimation for a temporarily disappeared pose using past and future frames, they exhibit large errors nevertheless. We present an energy minimization approach to generate smooth, valid trajectories in time, bridging gaps in visibility. We show that it is better than other interpolation based approaches and achieves state of the art results. In addition, we present the synthetic MuCo-Temp dataset, a temporal extension of the MuCo-3DHP dataset. Our code is made publicly available.
@article{arxiv.2011.00250,
title = {Temporal Smoothing for 3D Human Pose Estimation and Localization for Occluded People},
author = {Marton Veges and Andras Lorincz},
journal= {arXiv preprint arXiv:2011.00250},
year = {2020}
}