  For 3D hand and body pose estimation task in depth image, a novel
anchor-based approach termed Anchor-to-Joint regression network (A2J) with the
end-to-end learning ability is proposed. Within A2J, anchor points able to
capture global-local spatial context information are densely set on depth image
as local regressors for the joints. They contribute to predict the positions of
the joints in ensemble way to enhance generalization ability. The proposed 3D
articulated pose estimation paradigm is different from the state-of-the-art
encoder-decoder based FCN, 3D CNN and point-set based manners. To discover
informative anchor points towards certain joint, anchor proposal procedure is
also proposed for A2J. Meanwhile 2D CNN (i.e., ResNet-50) is used as backbone
network to drive A2J, without using time-consuming 3D convolutional or
deconvolutional layers. The experiments on 3 hand datasets and 2 body datasets
verify A2J's superiority. Meanwhile, A2J is of high running speed around 100
FPS on single NVIDIA 1080Ti GPU.
