A Point Distribution Model requires first the choice of an appropriate representation for the data and then the estimation of the density within this representation. Independent Component Analysis is a linear transform that represents the data in a space where statistical dependencies between the components are minimized. In this paper, we propose Independent Component Analysis as a representation for point distributions. We observe that within this representation, the density estimation is greatly simplified and propose solutions to the most common problems concerning shapes. Mainly, testing shape feasibility and finding the nearest feasible shape. We also observe how the description of shape deformations in terms of statistically independent modes provides a more intuitive and manageable framework. We perform experiments to illustrate the results and compare them with existing approaches.