Can Your Model Separate Yolks with a Water Bottle? Benchmarking Physical Commonsense Understanding in Video Generation Models

ICLR 2026 Conference Submission18831 Authors

Keywords: benchmarks, video generation, physical commonsense, text-to-video, generative models, evaluation, multimodal reasoning, synthetic video, model assessment

Abstract: Recent advances in text-to-video (T2V) generation have enabled visually compelling outputs, but models still struggle with everyday physical commonsense, often producing videos that violate intuitive expectations of causality, object behavior, and tool use. We introduce PhysVidBench, a human-validated benchmark for assessing physical reasoning in T2V models. It comprises carefully curated prompts spanning seven dimensions of physical interaction, from material transformation to temporal dynamics, offering broad, multi-faceted coverage of scenarios where physical plausibility is critical. For each prompt, we generate videos using diverse state-of-the-art models, and evaluate them through a three-stage pipeline: grounded physics questions are derived from each prompt, generated videos are captioned with a vision–language model, and a language model answers the questions using only the captions. This strategy mitigates hallucination and produces scores that align closely with human judgments. Beyond evaluation, PhysVidBench also serves as a diagnostic tool, enabling feedback-driven refinement of model outputs. By emphasizing affordances and tool-mediated actions, areas often overlooked in existing benchmarks, PhysVidBench provides a structured, interpretable framework for assessing and improving everyday physical commonsense in T2V models.

Primary Area: datasets and benchmarks

Submission Number: 18831