构建首个真实世界物理系统逆向恢复基准，支持多体动力学参数与方程识别。
IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video
- 设计4K/60fps真实视频数据集，覆盖单体与多体系统，含真实参数与误差估计。
- 提出标准化评估协议，涵盖参数精度、可识别性、外推能力等五项指标。
- 开源全套数据与基线模型，助力物理系统建模与方程发现研究。
从视频中无监督估计物理参数缺乏统一基准：现有方法在非重叠合成数据上评估，唯一真实数据集仅限单体系统，且无统一的方程识别协议。本文提出IRIS，一个高保真基准，包含240段4K分辨率、60fps的真实世界视频，涵盖单体与多体动力学系统，并配有独立测量的真值参数与不确定性估计。每个系统均在受控实验室条件下录制并配对真实微分方程，支持严格评估。定义了涵盖参数精度、可识别性、外推、鲁棒性及方程选择的标准评估协议。评估多种基线，包括多步物理损失法与四种互补方程识别策略（视觉语言模型时序推理、描述-分类提示、基于CNN的分类、路径标注），建立各场景下参考性能，揭示系统性失败模式，推动未来研究。数据集、标注、评估工具包及所有基线实现均已公开。
原文摘要 · Abstract (English)
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.
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