# PhySimCode：物理视频到代码生成的基准与评估方法

> 原文链接：https://robotics.hellobike.com/papers/2026-10-11-2610-15722
> 频道：papers | 更新日期：2026-10-11 | 标签：arxiv, cs.RO
> 核心摘要：提出PhySimCode，让模型从原始物理视频生成可运行Python仿真，并在程序生成样本上评估MLLM的视频到仿真能力。

# PhySimCode：物理视频到代码生成的基准与评估方法

::: v-pre
> 原文链接：[PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation](https://arxiv.org/abs/2610.15722)（arXiv 2610.15722 · 发布日期：2026-10-11）

**PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation**

提出PhySimCode，让模型从原始物理视频生成可运行Python仿真，并在程序生成样本上评估MLLM的视频到仿真能力。

## 核心要点

- 现有视频物理基准难以判断模型能否把视觉动力学转为可运行仿真代码。
- PhySimCode要求仅凭原始视频推断物理规律、参数，并生成复现视频的Python。
- 数据集含160.6K个程序生成样本，覆盖162种物理现象及配套代码、参数和视频。
- 评估十个MLLM发现其会陈述物理定律，但视频推断、参数恢复和仿真等价仍弱。

**验证范围与局限**：验证基于程序生成的SciPy仿真与PyBullet渲染场景，以及2,430个视频评估集。

## 英文摘要 (Abstract)

Multimodal large language models (MLLMs) are good at video understanding and code generation, but it remains unclear whether they can recover the physical laws underlying videos, a capability essential for converting videos into executable simulations and for providing structured physical supervision in robotics. Captioning motion video does not imply that a model has inferred its governing dynamics, estimated quantitative parameters, or can regenerate the observed scene from code. Existing video-physics benchmarks evaluate plausibility, property estimation, or future-state prediction, but offer limited insight into whether models can translate visual dynamics into runnable simulation code. We introduce PhySimCode, a benchmark for physics video-to-code generation: given only a raw video, a model must infer the relevant physics, recover parameter values, and generate runnable Python that reproduces the input video. The dataset contains 160.6K procedurally generated samples spanning 162 physics phenomena across analytic SciPy simulations and PyBullet-rendered rigid-body, articulated, fluid, and contact-rich scenes; each sample bundles the simulation code, physical parameters, rendered video, and a structured chain-of-thought covering observations, laws, ODEs, numerical methods, and validation checks. We evaluate ten state-of-the-art closed- and open-source MLLMs along physical-law correctness, simulation equivalence, code executability, and parameter recovery. Models reliably state physics laws (4.27-4.93/5) yet score only 1.51-2.47/5 on inferring them from video, and the best system (Claude Sonnet 4.6) recovers just 7.7% of ground-truth parameters within $\pm 20\%$, exposing a substantial video-to-simulation gap. The 2,430-video evaluation set is available at https://huggingface.co/datasets/SourajaKundu123/PhySimCode and the evaluation code at https://github.com/SourajaKundu/PhySimCode

作者：Souraja Kundu, Aditya Gupta, Joel Julin, Yizhou Zhao, Liuyue Xie, László A. Jeni
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## 原始参考来源 (Sources)

- [arXiv 2610.15722：PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation](https://arxiv.org/abs/2610.15722)