—The goal of this work is to develop statistical models for the shape change of a configuration of “landmark” points (key points of interest) over time and to use these models for filtering, tracking to automatically extract landmarks, synthesis and change detection. The term “shape activity” was introduced in recent work to denote a particular stochastic model for the dynamics of landmark shapes (dynamics after global translation, scale and rotation effects are normalized for). In that work, only models for stationary shape sequences were proposed. But most “activities” of a set of landmarks, e.g. running, jumping or crawling have large shape changes w.r.t initial shape and hence nonstationary. The key contribution of this work is a novel approach to define a generative model for both 2D and 3D nonstationary landmark shape sequences. We demonstrate the use of our nonstationary model for (a) sequentially filtering noise-corrupted landmark configurations to compute Mi...