Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.
@article{arxiv.1802.09232,
title = {2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning},
author = {Diogo C. Luvizon and David Picard and Hedi Tabia},
journal= {arXiv preprint arXiv:1802.09232},
year = {2018}
}