  Articulated hand pose and shape estimation is an important problem for
vision-based applications such as augmented reality and animation. In contrast
to the existing methods which optimize only for joint positions, we propose a
fully supervised deep network which learns to jointly estimate a full 3D hand
mesh representation and pose from a single depth image. To this end, a CNN
architecture is employed to estimate parametric representations i.e. hand pose,
bone scales and complex shape parameters. Then, a novel hand pose and shape
layer, embedded inside our deep framework, produces 3D joint positions and hand
mesh. Lack of sufficient training data with varying hand shapes limits the
generalized performance of learning based methods. Also, manually annotating
real data is suboptimal. Therefore, we present SynHand5M: a million-scale
synthetic dataset with accurate joint annotations, segmentation masks and mesh
files of depth maps. Among model based learning (hybrid) methods, we show
improved results on our dataset and two of the public benchmarks i.e. NYU and
ICVL. Also, by employing a joint training strategy with real and synthetic
data, we recover 3D hand mesh and pose from real images in 3.7ms.
