  3D hand pose estimation from single depth image is an important and
challenging problem for human-computer interaction. Recently deep convolutional
networks (ConvNet) with sophisticated design have been employed to address it,
but the improvement over traditional random forest based methods is not so
apparent. To exploit the good practice and promote the performance for hand
pose estimation, we propose a tree-structured Region Ensemble Network (REN) for
directly 3D coordinate regression. It first partitions the last convolution
outputs of ConvNet into several grid regions. The results from separate
fully-connected (FC) regressors on each regions are then integrated by another
FC layer to perform the estimation. By exploitation of several training
strategies including data augmentation and smooth $L_1$ loss, proposed REN can
significantly improve the performance of ConvNet to localize hand joints. The
experimental results demonstrate that our approach achieves the best
performance among state-of-the-art algorithms on three public hand pose
datasets. We also experiment our methods on fingertip detection and human pose
datasets and obtain state-of-the-art accuracy.
