3D Convolutional Neural Networks for Efficient and Robust
Hand Pose Estimation from Single Depth Images
Abstract
We propose a simple, yet effective approach for real-time
hand pose estimation from single depth images using threedimensional Convolutional Neural Networks (3D CNNs).
Image based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of
3D spatial information. Our proposed 3D CNN taking a 3D
volumetric representation of the hand depth image as input
can capture the 3D spatial structure of the input and accurately regress full 3D hand pose in a single pass. In order
to make the 3D CNN robust to variations in hand sizes and
global orientations, we perform 3D data augmentation on
the training data. Experiments show that our proposed 3D
CNN based approach outperforms state-of-the-art methods
on two challenging hand pose datasets, and is very efficient
as our implementation runs at over 215 fps on a standard
computer with a single GPU