3D Shape Estimation Based on Density Driven Model Fitting

We introduce a generic and efficient method for 2D and 3D shape estimation via density Þelds. Our method models shape as a density map and uses the notion of density to Þt 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 Þtting. With primary focus on estimating 3D shapes of non-rigid articulated objects such as human bodies, we illustrate our approach with examples of efficient model Þtting to 3D occupancy maps of human Þgures. We also discuss a number of extensions of our method to applications involving non-rigid object tracking and movement analysis.