  Since the emergence of large annotated datasets, state-of-the-art hand pose
estimation methods have been mostly based on discriminative learning. Recently,
a hybrid approach has embedded a kinematic layer into the deep learning
structure in such a way that the pose estimates obey the physical constraints
of human hand kinematics. However, the existing approach relies on a single
person's hand shape parameters, which are fixed constants. Therefore, the
existing hybrid method has problems to generalize to new, unseen hands. In this
work, we extend the kinematic layer to make the hand shape parameters
learnable. In this way, the learnt network can generalize towards arbitrary
hand shapes. Furthermore, inspired by the idea of Spatial Transformer Networks,
we apply a cascade of appearance normalization networks to decrease the
variance in the input data. The input images are shifted, rotated, and globally
scaled to a similar appearance. The effectiveness and limitations of our
proposed approach are extensively evaluated on the Hands 2017 challenge dataset
and the NYU dataset.
