We propose a novel, efficient deforming shape analysis and correspondence framework for animated meshes based on their dynamic and motion properties. We elaborate our method by exploiting a profitable set of motion data exhibited by deforming meshes with time-varying embedding. The main idea of our approach is based on an observation that a dynamic, deforming shape of a given subject contains much more information than a single static posture of it. This distinguishes our method from the existing methods which rely on static shape information for shape correspondence and analysis. Our framework of deforming shape analysis and correspondence of animated meshes is comprised of several major contributions: a new dynamic feature detection technique based on multi-scale animated mesh’s deformation characteristics, novel dynamic feature descriptor, and an adaptation of a robust graph-based feature correspondence approach followed by the fine matching of the animated meshes. We further use dynamic feature correspondences on the source and target to guide an iterative fine matching of animated meshes in spherical parameterization. We demonstrate advantages of our methods on different animated meshes of varying subjects, movements, complexities and details. .