title: Do generative video models learn physical principles from watching
    videos?
summary: 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.
paper_authors._id: 678a20d55a84f1087bd61c82
paper_authors.name: Saman Motamed
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:51:43.421Z
paper_authors._id: 6475c37b04c82116f9bb2356
paper_authors.avatarUrl: /avatars/6ec34eb3cfd091a38454ac3de72aaddc.svg
paper_authors.fullname: saman motamed
paper_authors.type: user
paper_authors.user: sam-motamed
paper_authors._id: 678a20d55a84f1087bd61c83
paper_authors.name: Laura Culp
paper_authors._id: 678a20d55a84f1087bd61c84
paper_authors.name: Kevin Swersky
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:51:57.327Z
paper_authors._id: 630e6ef664f1f8d0c771b758
paper_authors.avatarUrl: /avatars/1df2bfadb2b6fdf8307189936efc6ef0.svg
paper_authors.fullname: Kevin Swersky
paper_authors.type: user
paper_authors.user: kswersky
paper_authors._id: 678a20d55a84f1087bd61c85
paper_authors.name: Priyank Jaini
paper_authors._id: 678a20d55a84f1087bd61c86
paper_authors.name: Robert Geirhos
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:52:10.657Z
paper_authors._id: 673bbe0d7dfcdedd52619ec2
paper_authors.avatarUrl: /avatars/531a44f05d0c738bbe3e028c76c2e948.svg
paper_authors.fullname: Robert Geirhos
paper_authors.type: user
paper_authors.user: rgeirhos
paper_title: Do generative video models learn physical principles from watching
    videos?
paper_summary: 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.
paper_ai_keywords: Physics-IQ benchmark dataset fluid dynamics optics solid mechanics magnetism thermodynamics visual realism physical principles VideoPoet Stable Video Diffusion Visual Understanding