Over the years, computer vision researchers have developed a number of algorithms to solve a large number of problems. However, most of the existing algorithms are not characterized in terms of their performance, accuracy, cost, etc. Consequently, it is hardly ever possible to compare and choose between these various algorithms to tackle a specific problem.One of the contributions of this paper is the introduction of a framework for evaluating the performance of optical flow estimators, which is based on classical estimation theory criteria, and on considerations about the computation cost. This framework is general, and may be applied to other estimation problems.The optic flow is widely used in many vision systems. It is a vector velocity field defined on sequences of images. The affine optic flow is formed by the optical flow together with its first-order derivatives with respect to image coordinates. As a second contribution, we present two new estimators for the affine flow. We justify theoretically their design with hypotheses concerning the input images, which we show to be empirically valid.Finally, we use the performance analysis framework in order to compare the affine flow estimators with a more classical differential method.