  Low-cost consumer depth cameras and deep learning have enabled reasonable 3D
hand pose estimation from single depth images. In this paper, we present an
approach that estimates 3D hand pose from regular RGB images. This task has far
more ambiguities due to the missing depth information. To this end, we propose
a deep network that learns a network-implicit 3D articulation prior. Together
with detected keypoints in the images, this network yields good estimates of
the 3D pose. We introduce a large scale 3D hand pose dataset based on synthetic
hand models for training the involved networks. Experiments on a variety of
test sets, including one on sign language recognition, demonstrate the
feasibility of 3D hand pose estimation on single color images.
