We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Instead of computing candidate poses in individual frames and then linking them, as is often done, we regress directly from a spatio-temporal block of frames to a 3D pose in the central one. We will demonstrate that this approach allows us to effectively overcome ambiguities and to improve upon the state-of-the-art on challenging sequences.
@article{arxiv.1504.08200,
title = {Predicting People's 3D Poses from Short Sequences},
author = {Bugra Tekin and Xiaolu Sun and Xinchao Wang and Vincent Lepetit and Pascal Fua},
journal= {arXiv preprint arXiv:1504.08200},
year = {2015}
}