We wish to simulate motion using a stop-motion like animation technique. Here a non-rigid object is moved into a certain pose, an image is taken, the object's pose is altered slightly, another image is taken and so on. The result is a sequence that, when played at an interactive frame rate, provides the illusion of motion. We will apply the same principle here, in generating a benchmarking data set for NRSFM with a twist. That being in addition to acquiring an image, we also scan the object using a structured light scanner. This way, we acquire an image sequence which can be fed to a NRSFM algorithm and a 3D reference which can be used to benchmark the result. As an additional bonus, the stop-motion technique gives us great freedom to produce many different scenes that may provide varied challenges for NRSFM algorithms. Examples of these would be tearing, deformation, cracking etc.