mlsanityData to 14 Oct 2026

How Does Geometry Enter Generated Motion?

arXiv 2610.05135 ↗First posted 4 Oct 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Under a fixed physical law, the visible geometry of a scene determines how motion must change. We ask how video generators realize this relationship. We fix the law and the initial state and change only the geometry drawn in the first frame, within matched families of tracks and deflectors, and compare each generated trajectory with the simulator prediction for that geometry. Paired interventions change one thing at a time: a local bump, the height of a barrier, the words of the prompt, the length of the clip. Across nine image-to-video models, geometry is preserved and shapes the motion: the speed of the ball follows the drawn undulation of a track. A physical state would carry this response forward, and here the generated motion parts from the law. The mean slope barely accelerates the ball, successive contacts fail to compose through a consistent state, an edit ahead of the ball alters its motion before it arrives, and the ball climbs over barriers higher than its release point. Two global conditions organize the global trajectory: text strongly controls the destination, while clip length strongly controls timing in the open-weight models tested. The pattern persists with photographed first frames. Current video generation thus behaves as geometry-conditioned motion synthesis whose evolution of state differs systematically from that of a fixed physical law.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 4 Oct 2026.

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24 of 34 references.

References · 24

  1. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  2. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
  3. 2026LTX-2: Efficient Joint Audio-Visual Foundation Modelcited by 236
  4. 2024VideoPhy: Evaluating Physical Commonsense for Video Generationcited by 228
  5. 2024Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generationcited by 174
  6. 2026Cosmos 3: Omnimodal World Models for Physical AIcited by 165
  7. 2025Do generative video models understand physical principles?cited by 161
  8. 2026Seedance 2.0: Advancing Video Generation for World Complexitycited by 160
  9. 2025Evaluating Newtonian Mechanics in Video Generative Models with Real Physical Systemscited by 48
  10. 2026Physics-IQ Verifiedcited by 9
  11. 2026The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamicscited by 8
  12. 2025Objects in Generated Videos Are Slower Than They Appear: Models Suffer Sub-Earth Gravity and Don't Know Galileo's Principle...for nowcited by 5
12 more
  1. 2026Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Modelscited by 4
  2. 2026RigidBench: Evaluating Rigid-Body Physics in Video Generation Modelscited by 4
  3. 2026Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Themcited by 3
  4. 2026CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?cited by 3
  5. 2026Benchmarking Single-Factor Physical Video-to-Audio Generationcited by 2
  6. 2026The Seriality Gap in Video Diffusion Modelscited by 2
  7. 2026Principia: Relational Physics Tests for Video Modelscited by 2
  8. 2026PhysWeep: Does a Video Generator Realize the Physics You Ask For?cited by 2
  9. 2026Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanismscited by 2
  10. 2026MotionSpec: Spectral Trajectory Supervision for Motion-Consistent Video Generationcited by 1
  11. 2026MultiEcho: An Experimental Science of Learned Worldscited by 1
  12. 2026Foresight at the Event Boundary: Evaluating Physical Prediction in Video World Modelscited by 1

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