  This paper proposes a method for hand pose estimation from RGB images that
uses both external large-scale depth image datasets and paired depth and RGB
images as privileged information at training time. We show that providing depth
information during training significantly improves performance of pose
estimation from RGB images during testing. We explore different ways of using
this privileged information: (1) using depth data to initially train a
depth-based network, (2) using the features from the depth-based network of the
paired depth images to constrain mid-level RGB network weights, and (3) using
the foreground mask, obtained from the depth data, to suppress the responses
from the background area. By using paired RGB and depth images, we are able to
supervise the RGB-based network to learn middle layer features that mimic that
of the corresponding depth-based network, which is trained on large-scale,
accurately annotated depth data. During testing, when only an RGB image is
available, our method produces accurate 3D hand pose predictions. Our method is
also tested on 2D hand pose estimation. Experiments on three public datasets
show that the method outperforms the state-of-the-art methods for hand pose
estimation using RGB image input.
