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pretty_name: VisPhyBench
license: mit
language:
- en
---
# VisPhyBench
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
- **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{visphybench2026,
title = {VisPhyBench},
author = {Liang, Jiarong and Ku, Max and Hui, Ka-Hei and Nie, Ping and Chen, Wenhu},
howpublished = {GitHub repository},
year = {2026},
url = {https://github.com/TIGER-AI-Lab/VisPhyWorld}
}
```
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