IntPhys: A Benchmark for Visual Intuitive Physics Reasoning

Ronan Alexandre Riochet, Mario Ynocente Castro, Mathieu Bermard, Adam Lerer, Rob Fergus, Veronique Izard, Emmanuel Dupoux

Research output: Contribution to journalArticlepeer-review


In order to reach human performance on complex visual tasks, artificial systems need to incorporate a significant amount of understanding of the world in terms of macroscopic objects, movements, forces, etc. Inspired by work on intuitive physics in infants, we propose an evaluation benchmark which diagnoses how much a given system understands about physics by testing whether it can tell apart well matched videos of possible versus impossible events constructed with a game engine. The test requires systems to compute a physical plausibility score over an entire video. It is free of bias and can test a range of specific physical reasoning skills. We then describe two Deep Neural Networks systems aimed at learning intuitive physics in an unsupervised way, using only physically possible videos. The systems are trained with a future semantic mask prediction objective and tested on the possible versus impossible discrimination task. The analysis of their results compared to human data gives novel insights in the potentials and limitations of next frame prediction architectures.

Original languageEnglish (US)
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
StateAccepted/In press - 2021


  • Benchmark testing
  • Motion pictures
  • Physics
  • Predictive models
  • Shape
  • Task analysis
  • Visualization

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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