Abstract
Modern image editing models can satisfy a text instruction while breaking the physics of the edited scene. A new object may cast no shadow, a mirror may fail to reflect visible geometry, or an object may float above a surface that should support it. We study physical plausibility diagnosis, detecting whether an edited image violates scene physics, naming the violation type, localizing the affected region, and explaining the failure in language. We introduce a counterfactual benchmark whose controlled synthetic component uses Mitsuba~3 to generate 5,500 images from 500 scene families. Each family contains one clean image and ten matched violations involving shadows, reflection, support, surface response, and occlusion. The renderer pipeline provides category labels, affected-region masks and boxes, scene metadata, and explanation targets. We use LLaVA-1.5-7B, Qwen2.5-VL-7B, and InternVL3.5-8B as diagnostic baselines rather than proposed methods. On a 1,650-image synthetic test set, the adapted baselines reach 64.0--67.8\% category macro-F1 on standard held-out scenes. For LLaVA-1.5-7B, category macro-F1 falls from 64.0\% on the standard split to 40.8\% under intervention shift. This gap shows that high in-distribution accuracy partly reflects cues tied to rendering and counterfactual construction.
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Publication details
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Cite this article
APA 7
Esfahani, M. M., Salem, S., Alser, M., & Calhoun, V. (2026). Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency. https://omanscience.com/en/articles/do-vision-models-learn-physical-constraints-or-rendering-shortcuts-a-counterfactual-benchmark-for-grounded-physical-consistency
MLA 9
Esfahani, M. Moein, et al. "Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency." https://omanscience.com/en/articles/do-vision-models-learn-physical-constraints-or-rendering-shortcuts-a-counterfactual-benchmark-for-grounded-physical-consistency.
Chicago (author–date)
Esfahani, M. Moein, Sepehr Salem, Mohammed Alser, and Vince Calhoun. 2026. "Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency." https://omanscience.com/en/articles/do-vision-models-learn-physical-constraints-or-rendering-shortcuts-a-counterfactual-benchmark-for-grounded-physical-consistency.
Harvard
Esfahani, M. M., Salem, S., Alser, M. and Calhoun, V. (2026) 'Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency', Available at: https://omanscience.com/en/articles/do-vision-models-learn-physical-constraints-or-rendering-shortcuts-a-counterfactual-benchmark-for-grounded-physical-consistency.
Vancouver
Esfahani MM, Salem S, Alser M, Calhoun V. Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency. https://omanscience.com/en/articles/do-vision-models-learn-physical-constraints-or-rendering-shortcuts-a-counterfactual-benchmark-for-grounded-physical-consistency
IEEE
M. M. Esfahani, S. Salem, M. Alser, and V. Calhoun, "Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency," https://omanscience.com/en/articles/do-vision-models-learn-physical-constraints-or-rendering-shortcuts-a-counterfactual-benchmark-for-grounded-physical-consistency.