Learning Spatiotemporal Models from Training Examples

Physically based vibration modes have been shown to provide a useful mechanism for describing non-rigid motions of articulated and deformable objects. The approach relies on assumptions being made about the elastic properties of an object to generate a compact set of orthogonal shape parameters which can then be used for tracking and data approximation. We present a method for automatically generating an equivalent physically based model using a training set of examples of the object deforming over short time intervals. The resulting model provides a low dimensional shape description that allows accurate temporal extrapolation based on the training motions.