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

DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth