| --- |
| language: |
| - en |
| license: mit |
| pretty_name: VisPhyBench |
| task_categories: |
| - video-text-to-text |
| --- |
| |
| # VisPhyBench |
|
|
| [**Project Page**](https://tiger-ai-lab.github.io/VisPhyWorld/) | [**Paper**](https://huggingface.co/papers/2602.13294) | [**GitHub**](https://github.com/TIGER-AI-Lab/VisPhyWorld) |
|
|
| To evaluate how well models reconstruct appearance and reproduce physically plausible motion, we introduce VisPhyBench, a unified evaluation protocol comprising 209 scenes derived from 108 physical templates that assesses physical understanding through the lens of code-driven resimulation in both 2D and 3D scenes, integrating metrics from different aspects. Each scene is also annotated with a coarse difficulty label (easy/medium/hard). |
|
|
| # Dataset Details |
|
|
| - **Created by:** Jiarong Liang, Max Ku, Ka-Hei Hui, Ping Nie, Wenhu Chen |
| - **Language(s) (NLP):** English |
| - **License:** MIT |
| - **Repository:** `https://github.com/TIGER-AI-Lab/VisPhyWorld` |
|
|
| # Uses |
|
|
| The dataset is used to evaluate how well models reconstruct appearance and reproduce physically plausible motion. |
|
|
| # Dataset Structure |
|
|
| The difficulty of the two sets of VisPhyBench splits, **sub (209)** and **test (49)**, are as follows: **sub** is **114/67/28** (**54.5%/32.1%/13.4%**), and **test** is **29/17/3** (**59.2%/34.7%/6.1%**) (Easy/Medium/Hard). |
|
|
| ## What each sample contains |
|
|
| VisPhyBench is provided as two splits: |
|
|
| - `sub`: a larger split intended for evaluation and analysis. |
| - `test`: a smaller split subsampled from `sub` for quick sanity checks. |
|
|
| For each sample, we provide: |
|
|
| 1. **A short video** of a synthetic physical scene. |
| 2. **A detection JSON** (per sample) that describes the scene in the first frame. |
| 3. **A difficulty label** (easy/medium/hard) derived from the mean of eight annotators’ 1–5 ratings. |
|
|
| ## Detection JSON format |
|
|
| Each detection JSON includes: |
|
|
| - `image_size`: the image width/height. |
| - `coordinate_system`: conventions for coordinates (e.g., origin and axis directions). |
| - `objects`: a list of detected objects. Each object includes: |
| - `id`: unique identifier. |
| - `category`: coarse geometry category. |
| - `color_rgb`: RGB color triplet. |
| - `position`: object position (e.g., center coordinates). |
| - `bbox`: bounding box coordinates and size. |
| - `size`: coarse size fields (e.g., radius/length/thickness). |
|
|
| These fields specify object locations and attributes precisely, which helps an LLM initialize objects correctly when generating executable simulation code. |
|
|
| # BibTeX |
|
|
| ```bibtex |
| @misc{liang2026visphyworld, |
| title={VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction}, |
| author={Jiarong Liang and Max Ku and Ka-Hei Hui and Ping Nie and Wenhu Chen}, |
| year={2026}, |
| eprint={2602.13294}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2602.13294}, |
| } |
| ``` |
