3D shape estimation based on density driven model fitting

We introduce a generic and efficient method for 2D and 3D shape estimation via density fields. Our method models shape as a density map and uses the notion of density to fit a model to a rapidly computed occupancy map of the foreground object. We show how to utilize hierarchical (pyramid-like) object segmentation data to regularize a hierarchical model fitting. With primary focus on estimating 3D shapes of nonrigid articulated objects such as human bodies, we illustrate our approach with examples of efficient model fitting to 3D occupancy maps of human figures. We also discuss a number of extensions of our method to applications involving nonrigid object tracking and movement analysis.