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Despite recent advances in 3D pose estimation of human hands, especiallythanks to the advent of CNNs and depth cameras, this task is still far frombeing 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 anovel hierarchical tree-like structured CNN, in which branches are trained tobecome specialized in predefined subsets of hand joints, called local poses. Wefurther fuse local pose features, extracted from hierarchical CNN branches, tolearn higher order dependencies among joints in the final pose by end-to-endtraining. Lastly, the loss function used is also defined to incorporateappearance and physical constraints about doable hand motion and deformation.Finally, we introduce a non-rigid data augmentation approach to increase theamount of training depth data. Experimental results suggest that feeding atree-shaped CNN, specialized in local poses, into a fusion network for modelingjoints correlations and dependencies, helps to increase the precision of finalestimations, outperforming state-of-the-art results on NYU and SyntheticHanddatasets.

End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data