  We propose an entirely data-driven approach to estimating the 3D pose of a
hand given a depth image. We show that we can correct the mistakes made by a
Convolutional Neural Network trained to predict an estimate of the 3D pose by
using a feedback loop. The components of this feedback loop are also Deep
Networks, optimized using training data. They remove the need for fitting a 3D
model to the input data, which requires both a carefully designed fitting
function and algorithm. We show that our approach outperforms state-of-the-art
methods, and is efficient as our implementation runs at over 400 fps on a
single GPU.
