Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model
This addresses the problem of robust and accurate hand tracking for human-computer interaction, though it is incremental with improvements over existing methods.
The paper tackles real-time marker-less hand tracking from multiple RGB cameras by proposing a generative method using a Sum of Anisotropic Gaussians (SAG) model for implicit hand shape representation, achieving better accuracy than previous methods and running at 25 fps.
Real-time marker-less hand tracking is of increasing importance in human-computer interaction. Robust and accurate tracking of arbitrary hand motion is a challenging problem due to the many degrees of freedom, frequent self-occlusions, fast motions, and uniform skin color. In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time. The main contributions include a new generative tracking method which employs an implicit hand shape representation based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is smooth and analytically differentiable making fast gradient based pose optimization possible. This shape representation, together with a full perspective projection model, enables more accurate hand modeling than a related baseline method from literature. Our method achieves better accuracy than previous methods and runs at 25 fps. We show these improvements both qualitatively and quantitatively on publicly available datasets.