  Hand-object pose estimation (HOPE) aims to jointly detect the poses of both a
hand and of a held object. In this paper, we propose a lightweight model called
HOPE-Net which jointly estimates hand and object pose in 2D and 3D in
real-time. Our network uses a cascade of two adaptive graph convolutional
neural networks, one to estimate 2D coordinates of the hand joints and object
corners, followed by another to convert 2D coordinates to 3D. Our experiments
show that through end-to-end training of the full network, we achieve better
accuracy for both the 2D and 3D coordinate estimation problems. The proposed 2D
to 3D graph convolution-based model could be applied to other 3D landmark
detection problems, where it is possible to first predict the 2D keypoints and
then transform them to 3D.
