Objects in Generated Videos Are Slower Than They Appear: Models Suffer Sub-Earth Gravity and Don't Know Galileo's Principle...for now
On the map: World models → Latent predictive world models
Abstract (arXiv)
Video generators are increasingly evaluated as potential world models, which requires them to encode and understand physical laws. We investigate their representation of a fundamental law: gravity. Out-of-the-box video generators consistently generate objects falling at an effectively slower acceleration. However, these physical tests are often confounded by ambiguous metric scale. We first investigate if observed physical errors are artifacts of these ambiguities (e.g., incorrect frame rate assumptions). We find that even temporal rescaling cannot correct the high-variance gravity artifacts. To rigorously isolate the underlying physical representation from these confounds, we introduce a unit-free, two-object protocol that tests the timing ratio t12/t22 = h1/h2, a relationship independent of g, focal length, and scale. This relative test reveals violations of Galileo's equivalence principle. We then demonstrate that this physical gap can be partially mitigated with targeted specialization. A lightweight low-rank adaptor fine-tuned on only 100 single-ball clips raises geff from 1.81,m/s2 to 6.43,m/s2 (reaching 65% of terrestrial gravity). This specialist adaptor also generalizes zero-shot to two-ball drops and inclined planes, offering initial evidence that specific physical laws can be corrected with minimal data.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 25 of its 48 references that are in the map. First posted on arXiv 1 Dec 2025.
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25 of 48 references.
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Other references · 24
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12 more
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- 2025Intuitive physics understanding emerges from self-supervised pretraining on natural videoscited by 66
- 2023Generative Models: What Do They Know? Do They Know Things? Let's Find Out!cited by 50
- 2025ATI: Any Trajectory Instruction for Controllable Video Generationcited by 42
- 2025NewtonGen: Physics-Consistent and Controllable Text-to-Video Generation via Neural Newtonian Dynamicscited by 40
- 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generationcited by 39
- 2025T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generationcited by 35
- 2025"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in Text-to-Video Modelscited by 34
- 2025TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibilitycited by 16
- 2025Visual Jenga: Discovering Object Dependencies via Counterfactual Inpaintingcited by 11
- 2025PhysVidBench: Language-Grounded Evaluation of Physical Commonsense in Text-to-Video Modelscited by 2
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Citing papers per year: 2026: 3.
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