  3D hand pose estimation from a single depth image plays an important role in
computer vision and human-computer interaction. Although recent hand pose
estimation methods using convolution neural network (CNN) have shown notable
improvements in accuracy, most of them have a limitation that they rely on a
complex network structure without fully exploiting the articulated structure of
the hand. A hand, which is an articulated object, is composed of six local
parts: the palm and five independent fingers. Each finger consists of
sequential-joints that provide constrained motion, referred to as a kinematic
chain. In this paper, we propose a hierarchically-structured convolutional
recurrent neural network (HCRNN) with six branches that estimate the 3D
position of the palm and five fingers independently. The palm position is
predicted via fully-connected layers. Each sequential-joint, i.e. finger
position, is obtained using a recurrent neural network (RNN) to capture the
spatial dependencies between adjacent joints. Then the output features of the
palm and finger branches are concatenated to estimate the global hand position.
HCRNN directly takes the depth map as an input without a time-consuming data
conversion, such as 3D voxels and point clouds. Experimental results on public
datasets demonstrate that the proposed HCRNN not only outperforms most 2D
CNN-based methods using the depth image as their inputs but also achieves
competitive results with state-of-the-art 3D CNN-based methods with a highly
efficient running speed of 285 fps on a single GPU.
