  We propose to combine recent Convolutional Neural Networks (CNN) models with
depth imaging to obtain a reliable and fast multi-person pose estimation
algorithm applicable to Human Robot Interaction (HRI) scenarios. Our hypothesis
is that depth images contain less structures and are easier to process than RGB
images while keeping the required information for human detection and pose
inference, thus allowing the use of simpler networks for the task. Our
contributions are threefold. (i) we propose a fast and efficient network based
on residual blocks (called RPM) for body landmark localization from depth
images; (ii) we created a public dataset DIH comprising more than 170k
synthetic images of human bodies with various shapes and viewpoints as well as
real (annotated) data for evaluation; (iii) we show that our model trained on
synthetic data from scratch can perform well on real data, obtaining similar
results to larger models initialized with pre-trained networks. It thus
provides a good trade-off between performance and computation. Experiments on
real data demonstrate the validity of our approach.
