With the increasing versatility of CMR, further understanding of intrinsic contractility of the myocardium can be achieved by performing subject-specific modeling by integrating structural and functional information available. The recent introduction of the virtual tagging framework allows for visualization of the localized deformation of the myocardium based on phase contrast myocardial velocity mapping. The purpose of this study is to examine the use of a non-linear, Kernel-Partial Least Squares Regression (K-PLSR) predictive motion modeling scheme for the virtual tagging framework. The method allows for the derivation of a compact non-linear deformation model such that the entire deformation field can be predicted by a limited number of control points. When applied to virtual tagging, the technique can be used to predictively guide the mesh refinement based on the motion of the coarse grid, thus greatly reducing the search space and increasing the convergence speed of the algorithm. The effectiveness and numerical accuracy of the proposed technique are assessed with both numerically simulated data sets and in vivo phase contrast CMR velocity mapping from a group of 7 subjects. The technique presented has a distinct advantage over the conventional mesh refinement scheme and brings CMR myocardial contractility analysis closer to routine clinical practice.