  We propose to leverage recent advances in reliable 2D pose estimation with
Convolutional Neural Networks (CNN) to estimate the 3D pose of people from
depth images in multi-person Human-Robot Interaction (HRI) scenarios. Our
method is based on the observation that using the depth information to obtain
3D lifted points from 2D body landmark detections provides a rough estimate of
the true 3D human pose, thus requiring only a refinement step. In that line our
contributions are threefold. (i) we propose to perform 3D pose estimation from
depth images by decoupling 2D pose estimation and 3D pose refinement; (ii) we
propose a deep-learning approach that regresses the residual pose between the
lifted 3D pose and the true 3D pose; (iii) we show that despite its simplicity,
our approach achieves very competitive results both in accuracy and speed on
two public datasets and is therefore appealing for multi-person HRI compared to
recent state-of-the-art methods.
