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Add VisPhyBench splits (sub/test) with videos, detection JSON, difficulty tables

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  1. README.md +67 -0
  2. STAGING_INFO.txt +2 -0
  3. sub/detection_json/task00000_000_frame_01.json +77 -0
  4. sub/detection_json/task00000_005_frame_01.json +77 -0
  5. sub/detection_json/task00000_019_frame_01.json +77 -0
  6. sub/detection_json/task00100_000_frame_01.json +129 -0
  7. sub/detection_json/task00100_002_frame_01.json +129 -0
  8. sub/detection_json/task00100_019_frame_01.json +128 -0
  9. sub/detection_json/task10000_000_frame_01.json +237 -0
  10. sub/detection_json/task10000_004_frame_01.json +185 -0
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  13. sub/detection_json/task10001_018_frame_01.json +218 -0
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  15. sub/detection_json/task10002_019_frame_01.json +147 -0
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  49. sub/detection_json/task10019_014_frame_01.json +223 -0
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README.md ADDED
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+ ---
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+ pretty_name: VisPhyBench
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+ license: mit
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+ language:
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+ - en
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+ ---
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+
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+ # VisPhyBench
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+
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+ 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).
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+
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+ # Dataset Details
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+
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+ - **Created by:** Jiarong Liang
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+ - **Language(s) (NLP):** English
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+ - **License:** MIT
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+ - **Repository:** `https://github.com/TIGER-AI-Lab/VisPhyWorld`
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+
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+ # Uses
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+
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+ The dataset is used to evaluate how well models reconstruct appearance and reproduce physically plausible motion.
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+
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+ # Dataset Structure
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+
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+ 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).
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+
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+ ## What each sample contains
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+
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+ VisPhyBench is provided as two splits:
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+
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+ - `sub`: a larger split intended for evaluation and analysis.
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+ - `test`: a smaller split subsampled from `sub` for quick sanity checks.
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+
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+ For each sample, we provide:
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+
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+ 1. **A short video** of a synthetic physical scene.
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+ 2. **A detection JSON** (per sample) that describes the scene in the first frame.
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+ 3. **A difficulty label** (easy/medium/hard) derived from the mean of eight annotators’ 1–5 ratings.
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+
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+ ## Detection JSON format
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+
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+ Each detection JSON includes:
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+
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+ - `image_size`: the image width/height.
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+ - `coordinate_system`: conventions for coordinates (e.g., origin and axis directions).
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+ - `objects`: a list of detected objects. Each object includes:
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+ - `id`: unique identifier.
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+ - `category`: coarse geometry category.
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+ - `color_rgb`: RGB color triplet.
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+ - `position`: object position (e.g., center coordinates).
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+ - `bbox`: bounding box coordinates and size.
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+ - `size`: coarse size fields (e.g., radius/length/thickness).
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+
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+ These fields specify object locations and attributes precisely, which helps an LLM initialize objects correctly when generating executable simulation code.
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+
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+ # BibTeX
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+
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+ ```bibtex
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+ @misc{visphybench2026,
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+ title = {VisPhyBench},
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+ author = {Liang, Jiarong and Ku, Max and Hui, Ka-Hei and Nie, Ping and Chen, Wenhu},
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+ howpublished = {GitHub repository},
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+ year = {2026},
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+ url = {https://github.com/TIGER-AI-Lab/VisPhyWorld}
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+ }
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+ ```
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+
STAGING_INFO.txt ADDED
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+ Staged from /home/jiarong/VisPhyWorld
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+ Created: 2026-02-08T21:36:22.284123Z
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