  Discriminative methods often generate hand poses kinematically implausible,
then generative methods are used to correct (or verify) these results in a
hybrid method. Estimating 3D hand pose in a hierarchy, where the
high-dimensional output space is decomposed into smaller ones, has been shown
effective. Existing hierarchical methods mainly focus on the decomposition of
the output space while the input space remains almost the same along the
hierarchy. In this paper, a hybrid hand pose estimation method is proposed by
applying the kinematic hierarchy strategy to the input space (as well as the
output space) of the discriminative method by a spatial attention mechanism and
to the optimization of the generative method by hierarchical Particle Swarm
Optimization (PSO). The spatial attention mechanism integrates cascaded and
hierarchical regression into a CNN framework by transforming both the input(and
feature space) and the output space, which greatly reduces the viewpoint and
articulation variations. Between the levels in the hierarchy, the hierarchical
PSO forces the kinematic constraints to the results of the CNNs. The
experimental results show that our method significantly outperforms four
state-of-the-art methods and three baselines on three public benchmarks.
