  Despite recent advances in 3D pose estimation of human hands, especially
thanks to the advent of CNNs and depth cameras, this task is still far from
being solved. This is mainly due to the highly non-linear dynamics of fingers,
which make hand model training a challenging task. In this paper, we exploit a
novel hierarchical tree-like structured CNN, in which branches are trained to
become specialized in predefined subsets of hand joints, called local poses. We
further fuse local pose features, extracted from hierarchical CNN branches, to
learn higher order dependencies among joints in the final pose by end-to-end
training. Lastly, the loss function used is also defined to incorporate
appearance and physical constraints about doable hand motion and deformation.
Finally, we introduce a non-rigid data augmentation approach to increase the
amount of training depth data. Experimental results suggest that feeding a
tree-shaped CNN, specialized in local poses, into a fusion network for modeling
joints correlations and dependencies, helps to increase the precision of final
estimations, outperforming state-of-the-art results on NYU and SyntheticHand
datasets.
