  We introduce and evaluate several architectures for Convolutional Neural
Networks to predict the 3D joint locations of a hand given a depth map. We
first show that a prior on the 3D pose can be easily introduced and
significantly improves the accuracy and reliability of the predictions. We also
show how to use context efficiently to deal with ambiguities between fingers.
These two contributions allow us to significantly outperform the
state-of-the-art on several challenging benchmarks, both in terms of accuracy
and computation times.
