A variational approach to monocular hand-pose estimation

In this paper, we propose a model-based approach to recover 3D hand pose from 2D images. To this end, we describe the hand structure using a compact 3D articulated model and reformulate pose estimation as a binary image segmentation problem aiming to separate the hand from the background. We propose generative models for hand and background pixels leading to a log-likelihood objective function which aims to enclose hand-like pixels within the projected silhouette of the 3D model while excluding background-like pixels. Segmentation and hand-pose estimation are jointly addressed through the minimization of a single likelihood function. Pose is determined through gradient descent in the hand parameter space of such an area-based objective function. Furthermore, we propose a new constrained variable metric gradient descent to speed up convergence and finally the so called smart particle filter to deal with occlusions and local minima through multiple hypotheses. Promising experimental results demonstrate the potentials of our approach.