In this paper we propose new algorithms for accurate nonrigid motion tracking. Given only a set of sparse correspondences and incomplete or missing information about geometry or material properties, we recover dense motion vectors using nonlinear finite element models. The method is based on the iterative analysis of the differences between the actual and predicted behavior. Large differences indicate that an object's properties are not captured properly by the model. Feedback from the images during the motion allows the refinement of the model by minimizing the error between the expected and true position of the object's points. Unknown parameters are recovered using an iterative descent search for the best model that approximates nonrigid motion of the given object. Thus, during tracking the model is refined which, in turn, improves tracking quality. The method was applied successfully to man-made elastic materials and human skin to recover unknown elasticity, to complex 3-D objects to find details of their geometry, and to a hand motion analysis application.
['Leonid V. Tsap', 'Dmitry B. Goldgof', 'Sudeep Sarkar']
Nonrigid motion analysis based on dynamic refinement of finite element models