  In order to reach human performance on complexvisual tasks, artificial
systems need to incorporate a sig-nificant amount of understanding of the world
in termsof macroscopic objects, movements, forces, etc. Inspiredby work on
intuitive physics in infants, we propose anevaluation benchmark which diagnoses
how much a givensystem understands about physics by testing whether itcan tell
apart well matched videos of possible versusimpossible events constructed with
a game engine. Thetest requires systems to compute a physical plausibilityscore
over an entire video. It is free of bias and cantest a range of basic physical
reasoning concepts. Wethen describe two Deep Neural Networks systems aimedat
learning intuitive physics in an unsupervised way,using only physically
possible videos. The systems aretrained with a future semantic mask prediction
objectiveand tested on the possible versus impossible discrimi-nation task. The
analysis of their results compared tohuman data gives novel insights in the
potentials andlimitations of next frame prediction architectures.
