  This work addresses a novel and challenging problem of estimating the full 3D
hand shape and pose from a single RGB image. Most current methods in 3D hand
analysis from monocular RGB images only focus on estimating the 3D locations of
hand keypoints, which cannot fully express the 3D shape of hand. In contrast,
we propose a Graph Convolutional Neural Network (Graph CNN) based method to
reconstruct a full 3D mesh of hand surface that contains richer information of
both 3D hand shape and pose. To train networks with full supervision, we create
a large-scale synthetic dataset containing both ground truth 3D meshes and 3D
poses. When fine-tuning the networks on real-world datasets without 3D ground
truth, we propose a weakly-supervised approach by leveraging the depth map as a
weak supervision in training. Through extensive evaluations on our proposed new
datasets and two public datasets, we show that our proposed method can produce
accurate and reasonable 3D hand mesh, and can achieve superior 3D hand pose
estimation accuracy when compared with state-of-the-art methods.
