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.

本论文提出了一项基于视觉任务的评估基准，以测试人工系统对于宏观物体、运动、力等物理理解的程度，同时介绍了两个旨在以无监督的方式学习直观物理的深度神经网络系统，并通过与人类数据的比较，对下一帧预测建筑的潜力和限制进行了新颖的结果分析。