IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video

Unsupervised physical parameter estimation from video lacksa common benchmark: existing methods evaluate on non-overlappingsynthetic data, the sole real-world dataset is restricted to single-bodysystems, and no established protocol addresses governing-equation identi-fication. This work introduces IRIS, a high-fidelity benchmark comprising240 real-world videos captured at 4K resolution and 60 fps, spanning bothsingle- and multi-body dynamics with independently measured ground-truth parameters and uncertainty estimates. Each dynamical systemis recorded under controlled laboratory conditions and paired with itsgoverning equations, enabling principled evaluation. A standardized evalu-ation protocol is defined encompassing parameter accuracy, identifiability,extrapolation, robustness, and governing-equation selection. Multiplebaselines are evaluated, including a multi-step physics loss formulationand four complementary equation-identification strategies (VLM tempo-ral reasoning, describe-then-classify prompting, CNN-based classification,and path-based labelling), establishing reference performance across allIRIS scenarios and exposing systematic failure modes that motivate futureresearch. The dataset, annotations, evaluation toolkit, and all baselineimplementations are publicly released.