  Depth cameras allow to set up reliable solutions for people monitoring and
behavior understanding, especially when unstable or poor illumination
conditions make unusable common RGB sensors. Therefore, we propose a complete
framework for the estimation of the head and shoulder pose based on depth
images only. A head detection and localization module is also included, in
order to develop a complete end-to-end system. The core element of the
framework is a Convolutional Neural Network, called POSEidon+, that receives as
input three types of images and provides the 3D angles of the pose as output.
Moreover, a Face-from-Depth component based on a Deterministic Conditional GAN
model is able to hallucinate a face from the corresponding depth image. We
empirically demonstrate that this positively impacts the system performances.
We test the proposed framework on two public datasets, namely Biwi Kinect Head
Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by
the automotive setup. Experimental results show that our method overcomes
several recent state-of-art works based on both intensity and depth input data,
running in real-time at more than 30 frames per second.
