| | --- |
| | 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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| |
|
