  We propose a novel 3D neural network architecture for 3D hand pose estimation
from a single depth image. Different from previous works that mostly run on 2D
depth image domain and require intermediate or post process to bring in the
supervision from 3D space, we convert the depth map to a 3D volumetric
representation, and feed it into a 3D convolutional neural network(CNN) to
directly produce the pose in 3D requiring no further process. Our system does
not require the ground truth reference point for initialization, and our
network architecture naturally integrates both local feature and global context
in 3D space. To increase the coverage of the hand pose space of the training
data, we render synthetic depth image by transferring hand pose from existing
real image datasets. We evaluation our algorithm on two public benchmarks and
achieve the state-of-the-art performance. The synthetic hand pose dataset will
be available.
