Dataset Viewer
The dataset could not be loaded because the splits use different data file formats, which is not supported. Read more about the splits configuration. Click for more details.
Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('test'): ('videofolder', {})}
Error code:   FileFormatMismatchBetweenSplitsError

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Physical-ICL

Physical-ICL is a project-level dataset repository for studying physical in-context learning in video generation.

The current version contains a preliminary subset built from Physics-IQ. Each sample is organized as a query video with candidate demonstration videos. These demonstrations are labeled by their relationship to the query, such as good, weak, opposite, or irrelevant demonstrations.

Current subset

Subset Path Description
Physics-IQ preliminary subset data/physiq_prelim/ A preliminary physical ICL dataset constructed from Physics-IQ videos.

Repository structure

data/
  physiq_prelim/
    gt_data/
      task_0001/
        episode_0001/
          video.mp4
          prompt/
            init_frame.png
            prompt.txt
          demos/
            good_demo_01.mp4
            good_demo_01.png
            weak_demo_01.mp4
            weak_demo_01.png
            opposite_demo_01.mp4
            opposite_demo_01.png
            irrelevant_demo_01.mp4
            irrelevant_demo_01.png
    summary.json
    case_summary.csv
    README.md
    export_warnings.txt
    storyboard_warnings.txt

Sample format

Each sample is stored under:

data/physiq_prelim/gt_data/task_xxxx/episode_0001/

The files have the following meanings:

File or folder Description
video.mp4 Query target video. In the current version, this uses the full Physics-IQ video when available.
prompt/init_frame.png Query initial frame, extracted from the first frame of the full query video.
prompt/prompt.txt Text prompt for the query video.
demos/*.mp4 Candidate demonstration videos. These use 5-second Physics-IQ testing clips.
demos/*.png 3x3 event-aware storyboard images generated from the corresponding demo video.

Demo types

Demo files are named by their coarse relationship to the query:

Filename pattern Meaning
good_demo_XX.mp4 A suitable positive demonstration.
weak_demo_XX.mp4 A weakly related demonstration.
opposite_demo_XX.mp4 A demonstration showing an opposite or contrastive physical outcome.
irrelevant_demo_XX.mp4 An unrelated or different-category control demonstration.

The corresponding .png file is a 3x3 storyboard extracted from the same demo video. For example:

good_demo_01.mp4
good_demo_01.png

Low-quality generated demonstrations are not included in the current version.

Metadata files

File Description
data/physiq_prelim/summary.json Machine-readable metadata for all samples and demonstrations.
data/physiq_prelim/case_summary.csv Human-readable case-level summary.
data/physiq_prelim/export_warnings.txt Export warnings, if any.
data/physiq_prelim/storyboard_warnings.txt Storyboard generation warnings, if any.

Metadata schema

Each item in summary.json corresponds to one query sample. The main fields are:

Field Description
case_id Unique case identifier.
task_name Task folder name.
gt_path Path to the query video.
image Path to the query initial frame.
prompt Query prompt.
query_scenario Physics-IQ scenario name for the query.
query_macro_group Coarse physical category.
query_event_tag Fine-grained event tag.
demos Candidate demonstrations for this query.
available_demo_types Available demo types for this query.

Each demo entry contains:

Field Description
demo_type One of good, weak, opposite, or irrelevant.
demo_path Path to the demo video.
demo_image_path Path to the 3x3 storyboard image.
demo_scenario Physics-IQ scenario name for the demo.
demo_relation More detailed relation label.
physical_similarity Physical similarity label.
visual_similarity Visual similarity label.

Usage example

Load the metadata:

import json
from pathlib import Path

root = Path("data/physiq_prelim")

with open(root / "summary.json", "r", encoding="utf-8") as f:
    items = json.load(f)

sample = items[0]

query_video = root.parent.parent / sample["gt_path"]
query_image = root.parent.parent / sample["image"]
query_prompt = sample["prompt"][0]

good_demos = [
    d for d in sample["demos"]
    if d["demo_type"] == "good"
]

For video-capable models, use demo_path. For image-only models, use demo_image_path.

Notes

  • Query videos use full Physics-IQ videos when available.
  • Demo videos use 5-second Physics-IQ testing clips.
  • Demo storyboard images are generated using event-aware 3x3 frame sampling.
  • The current version does not include low-quality generated demonstrations.
  • This repository is intended for research and preliminary experiments.
Downloads last month
2,069