Generating spatiotemporal models from examples

Recent work on contour tracking has shown the benefits of using learned dynamic models for robust curve tracking. In this paper, we extend this work by using a physically based framework to learn physically plausible, constrained dynamic models of deforming objects. Traditional physically based vibration modes have been shown to provide a useful mechanism for describing non-rigid motions of articulated and deformable objects. The standard approach relies on assumptions being made about the elastic properties of an object to generate a compact set of real, orthogonal shape parameters which can then be used for tracking and data approximation. We present a method for automatically generating an improved physically based model using a training set of examples of the object deforming, tuning the elastic properties of the object to reflect how the object actually deforms. The resulting model provides a low dimensional shape description that allows accurate temporal extrapolation at low computational cost based on the training motions. Results are shown in which the method is applied to an automatically acquired training set of the outline of a walking pedestrian.