  Hand pose estimation is more challenging than body pose estimation due to
severe articulation, self-occlusion and high dexterity of the hand. Current
approaches often rely on a popular body pose algorithm, such as the
Convolutional Pose Machine (CPM), to learn 2D keypoint features. These
algorithms cannot adequately address the unique challenges of hand pose
estimation, because they are trained solely based on keypoint positions without
seeking to explicitly model structural relationship between them. We propose a
novel Nonparametric Structure Regularization Machine (NSRM) for 2D hand pose
estimation, adopting a cascade multi-task architecture to learn hand structure
and keypoint representations jointly. The structure learning is guided by
synthetic hand mask representations, which are directly computed from keypoint
positions, and is further strengthened by a novel probabilistic representation
of hand limbs and an anatomically inspired composition strategy of mask
synthesis. We conduct extensive studies on two public datasets - OneHand 10k
and CMU Panoptic Hand. Experimental results demonstrate that explicitly
enforcing structure learning consistently improves pose estimation accuracy of
CPM baseline models, by 1.17% on the first dataset and 4.01% on the second one.
The implementation and experiment code is freely available online. Our proposal
of incorporating structural learning to hand pose estimation requires no
additional training information, and can be a generic add-on module to other
pose estimation models.
