  We present a system for real-time RGBD-based estimation of 3D human pose. We
use parametric 3D deformable human mesh model (SMPL-X) as a representation and
focus on the real-time estimation of parameters for the body pose, hands pose
and facial expression from Kinect Azure RGB-D camera. We train estimators of
body pose and facial expression parameters. Both estimators use previously
published landmark extractors as input and custom annotated datasets for
supervision, while hand pose is estimated directly by a previously published
method. We combine the predictions of those estimators into a temporally-smooth
human pose. We train the facial expression extractor on a large talking face
dataset, which we annotate with facial expression parameters. For the body pose
we collect and annotate a dataset of 56 people captured from a rig of 5 Kinect
Azure RGB-D cameras and use it together with a large motion capture AMASS
dataset. Our RGB-D body pose model outperforms the state-of-the-art RGB-only
methods and works on the same level of accuracy compared to a slower RGB-D
optimization-based solution. The combined system runs at 30 FPS on a server
with a single GPU. The code will be available at
https://saic-violet.github.io/rgbd-kinect-pose
