  Accurate hand pose estimation at joint level has several uses on human-robot
interaction, user interfacing and virtual reality applications. Yet, it
currently is not a solved problem. The novel deep learning techniques could
make a great improvement on this matter but they need a huge amount of
annotated data. The hand pose datasets released so far present some issues that
make them impossible to use on deep learning methods such as the few number of
samples, high-level abstraction annotations or samples consisting in depth
maps. In this work, we introduce a multiview hand pose dataset in which we
provide color images of hands and different kind of annotations for each, i.e
the bounding box and the 2D and 3D location on the joints in the hand. Besides,
we introduce a simple yet accurate deep learning architecture for real-time
robust 2D hand pose estimation.
