This paper presents a technique to learn dynamic appearance models from a small number of training frames. Under this framework, dynamic appearance is modelled as an unknown operator that satisfies certain interpolation conditions and that can be efficiently identified using very little a priori information with off the shelf software. The advantages of the proposed method are illustrated with several examples where the leaned dynamics accurately predict the appearance of the targets preventing tracking failures due to occlusion or clutter.