Do generative video models learn physical principles from watching
  videos?
Authors: name: Saman Motamed, name: Laura Culp, name: Kevin Swersky, name: Priyank Jaini, name: Robert Geirhos
Organization: Deepmind

AI video generation is undergoing a revolution, with quality and realism
advancing rapidly. These advances have led to a passionate scientific debate:
Do video models learn ``world models'' that discover laws of physics -- or,
alternatively, are they merely sophisticated pixel predictors that achieve
visual realism without understanding the physical principles of reality? We
address this question by developing Physics-IQ, a comprehensive benchmark
dataset that can only be solved by acquiring a deep understanding of various
physical principles, like fluid dynamics, optics, solid mechanics, magnetism
and thermodynamics. We find that across a range of current models (Sora,
Runway, Pika, Lumiere, Stable Video Diffusion, and VideoPoet), physical
understanding is severely limited, and unrelated to visual realism. At the same
time, some test cases can already be successfully solved. This indicates that
acquiring certain physical principles from observation alone may be possible,
but significant challenges remain. While we expect rapid advances ahead, our
work demonstrates that visual realism does not imply physical understanding.
Our project page is at https://physics-iq.github.io; code at
https://github.com/google-deepmind/physics-IQ-benchmark.