PCBench: Physical Commonsense Benchmark for Agentic Video Evaluation and Generation

Video generators now render photorealistic clips, but do the objects in them move as physics requires? Released balls hover, pendulums stall and requested collisions never happen, yet common visual-quality metrics and vision-language judges do not directly measure conformity to explicit motion laws. We introduce PCBench, a physical-commonsense benchmark that pairs 3,093 real videos in ten phenomenon categories with 4,049 generated videos from six generator families and 945 simulations with perturbations of known severity. Four-axis annotations of generated clips separate prompt following from physical plausibility. Across all six generators, the requested phenomenon is realized less reliably than general physical plausibility. For mechanics, where motion laws are explicit and can be checked quantitatively, we propose agentic evaluation: a tool-using workflow grounds and tracks the target object, fits an executable motion program and reports an interpretable Motion-Law Conformity (MLC) score. Without fitting to human labels, the residual increases with severity across all 18 controlled perturbation pairs, and MLC has the highest point correlation with mean human ratings on 216 generated mechanics clips ($\rho =0.38$ versus 0.21 for VideoScore2), although the paired interval for this difference includes zero. Because the program that scores a motion can also produce it, the same programs close the loop to generation: they render temporal controls for a video diffusion model, re-measure the output and repair measured failures. Program-controlled generation produces the requested motion where resampling rarely does, and three human raters on average favour it in all 16 matched pairs. Code is available through an anonymous link: https://anonymous.4open.science/r/pcbench-iclr27-review-code-2F15/