  We release two artificial datasets, Simulated Flying Shapes and Simulated
Planar Manipulator that allow to test the learning ability of video processing
systems. In particular, the dataset is meant as a tool which allows to easily
assess the sanity of deep neural network models that aim to encode, reconstruct
or predict video frame sequences. The datasets each consist of 90000 videos.
The Simulated Flying Shapes dataset comprises scenes showing two objects of
equal shape (rectangle, triangle and circle) and size in which one object
approaches its counterpart. The Simulated Planar Manipulator shows a 3-DOF
planar manipulator that executes a pick-and-place task in which it has to place
a size-varying circle on a squared platform. Different from other widely used
datasets such as moving MNIST [1], [2], the two presented datasets involve
goal-oriented tasks (e.g. the manipulator grasping an object and placing it on
a platform), rather than showing random movements. This makes our datasets more
suitable for testing prediction capabilities and the learning of sophisticated
motions by a machine learning model. This technical document aims at providing
an introduction into the usage of both datasets.
