  State-of-the-art single depth image-based 3D hand pose estimation methods are
based on dense predictions, including voxel-to-voxel predictions,
point-to-point regression, and pixel-wise estimations. Despite the good
performance, those methods have a few issues in nature, such as the poor
trade-off between accuracy and efficiency, and plain feature representation
learning with local convolutions. In this paper, a novel pixel-wise
prediction-based method is proposed to address the above issues. The key ideas
are two-fold: a) explicitly modeling the dependencies among joints and the
relations between the pixels and the joints for better local feature
representation learning; b) unifying the dense pixel-wise offset predictions
and direct joint regression for end-to-end training. Specifically, we first
propose a graph convolutional network (GCN) based joint graph reasoning module
to model the complex dependencies among joints and augment the representation
capability of each pixel. Then we densely estimate all pixels' offsets to
joints in both image plane and depth space and calculate the joints' positions
by a weighted average over all pixels' predictions, totally discarding the
complex postprocessing operations. The proposed model is implemented with an
efficient 2D fully convolutional network (FCN) backbone and has only about 1.4M
parameters. Extensive experiments on multiple 3D hand pose estimation
benchmarks demonstrate that the proposed method achieves new state-of-the-art
accuracy while running very efficiently with around a speed of 110fps on a
single NVIDIA 1080Ti GPU.
