This paper presents an affine model for 3-D motion and shape recovery using two perspective views and their relative 2-D displacement field. The 2-D displacement vectors are estimated as parameters of a 2-D affine model that generalizes standard block matching by allowing affine shape deformations of image blocks and affine intensity transformations. The matching block size is effectively found via morphological size histograms. The parameters of the 3-D affine model are estimated using a least-squares algorithm that requires solving a system of linear equations with rank three. Some stabilization of the recovered motion parameters under noise is achieved through a simple form of MAP estimation. A multi-scale searching in the parameter space is also used to improve accuracy without high computational cost. Experiments on applying these affine models to various real world image sequences demonstrate that they can estimate dense displacement fields and recover motion parameters and object shape with relatively small errors.