Constraints on deformable models: Recovering 3d shape and nonrigid motion

Abstract

Inferring the 3D structures of nonrigidly moving objects from images is a difficult yet basic problem in computational vision. Our approach makes use of dynamic, elastically deformable object models that offer the geometric flexibility to satisfy a diversity of real-world visual constraints. We specialize these models to include intrinsic forces inducing a preference for axisymmetry. Image-based constraints are applied as extrinsic forces that mold the symmetry-seeking model into shapes consistent with image data. We describe an extrinsic force that applies constraints derived from profiles of monocularly viewed objects. We generalize this constraint force to incorporate profile information from multiple views and use it to exploit binocular image data. For time-varying images, the force becomes dynamic and the model is able to infer not only depth, but nonrigid motion as well. We demonstrate the recovery of 3D shape and nonrigid motion from natural imagery.