---
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}
}
```