  We present a method for the real-time estimation of the full 3D pose of one
or more human hands using a single commodity RGB camera. Recent work in the
area has displayed impressive progress using RGBD input. However, since the
introduction of RGBD sensors, there has been little progress for the case of
monocular color input. We capitalize on the latest advancements of deep
learning, combining them with the power of generative hand pose estimation
techniques to achieve real-time monocular 3D hand pose estimation in
unrestricted scenarios. More specifically, given an RGB image and the relevant
camera calibration information, we employ a state-of-the-art detector to
localize hands. Given a crop of a hand in the image, we run the pretrained
network of OpenPose for hands to estimate the 2D location of hand joints.
Finally, non-linear least-squares minimization fits a 3D model of the hand to
the estimated 2D joint positions, recovering the 3D hand pose. Extensive
experimental results provide comparison to the state of the art as well as
qualitative assessment of the method in the wild.
