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

arXiv: https://arxiv.org/abs/2603.16432 · HTML: https://mlsanity.com/paper/2603.16432/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 28 of 28)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Learning to Simulate Complex Physics with Graph Networks](https://mlsanity.com/paper/2002.09405/) (2020)
- [Hamiltonian Neural Networks](https://mlsanity.com/paper/1906.01563/) (2019)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets](https://mlsanity.com/paper/2103.10974/) (2021)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [Lagrangian Neural Networks](https://mlsanity.com/paper/2003.04630/) (2020)
- [How to train your neural ODE: the world of Jacobian and kinetic regularization](https://mlsanity.com/paper/2002.02798/) (2020)
- [VideoPhy: Evaluating Physical Commonsense for Video Generation](https://mlsanity.com/paper/2406.03520/) (2024)
- [Do generative video models understand physical principles?](https://mlsanity.com/paper/2501.09038/) (2025)
- [Hamiltonian Generative Networks](https://mlsanity.com/paper/1909.13789/) (2019)
- [A Differentiable Physics Engine for Deep Learning in Robotics](https://mlsanity.com/paper/1611.01652/) (2016)
- [Visual Interaction Networks](https://mlsanity.com/paper/1706.01433/) (2017)
- [Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact](https://mlsanity.com/paper/2103.16021/) (2021)
- [Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video](https://mlsanity.com/paper/1905.11169/) (2019)
- [R-Bench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation](https://mlsanity.com/paper/2505.02018/) (2025)
- [WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World](https://mlsanity.com/paper/2512.10958/) (2025)
- [DiffSim: Taming Diffusion Models for Evaluating Visual Similarity](https://mlsanity.com/paper/2412.14580/) (2024)
- [Neural Implicit Representations for Physical Parameter Inference from a Single Video](https://mlsanity.com/paper/2204.14030/) (2022)
- [Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks](https://mlsanity.com/paper/1807.09244/) (2018)
- [Reasoning-Modulated Representations](https://mlsanity.com/paper/2107.08881/) (2021)
- [Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems](https://mlsanity.com/paper/2410.01376/) (2024)
- [Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models](https://mlsanity.com/paper/2412.08619/) (2024)
- [Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery](https://mlsanity.com/paper/2508.17380/) (2025)
- [ContactGaussian-WM: Learning Physics-Grounded World Model from Videos](https://mlsanity.com/paper/2602.11021/) (2026)
- [MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data](https://mlsanity.com/paper/2505.11940/) (2025)

## Built on by (most cited first, 4 of 4)

- [GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models](https://mlsanity.com/paper/2608.05948/) (2026)
- [One Perturbation Is Not Enough: Identifiability and Blind Baselines for Behavioral AI Evaluation](https://mlsanity.com/paper/2609.06190/) (2026)
- [Physics as the label for measuring and correcting materials reasoning in multimodal models](https://mlsanity.com/paper/2609.12181/) (2026)
- [PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction](https://mlsanity.com/paper/2610.17288/) (2026)

## Abstract (arXiv)

Unsupervised physical parameter estimation from video lacks a common benchmark: existing methods evaluate on non-overlapping synthetic data, the sole real-world dataset is restricted to single-body systems, and no established protocol addresses governing-equation identification. This work introduces IRIS, a high-fidelity benchmark comprising 240 real-world videos captured at 4K resolution and 60fps, spanning both single- and multi-body dynamics with independently measured ground-truth parameters and uncertainty estimates. Each dynamical system is recorded under controlled laboratory conditions and paired with its governing equations, enabling principled evaluation. A standardized evaluation protocol is defined encompassing parameter accuracy, identifiability, extrapolation, robustness, and governing-equation selection. Multiple baselines are evaluated, including a multi-step physics loss formulation and four complementary equation-identification strategies (VLM temporal reasoning, describe-then-classify prompting, CNN-based classification, and path-based labelling), establishing reference performance across all IRIS scenarios and exposing systematic failure modes that motivate future research. The dataset, annotations, evaluation toolkit, and all baseline implementations are publicly released.