Datasets:

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Physical-ICL

Error code:   DatasetGenerationError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  original_shard_lengths[original_shard_id] += len(table)
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
              IndexError: list index out of range
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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text string
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Show the rough semicircular track with height label h and a block at P; Generate the motion after releasing the block. Note that the track has friction.Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Dynamically depict the forces between magnets, paying attention to speed and intensity. Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Animate the ball reflecting at equal angles off the walls and landing near a lower-numbered brick. Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Animate the red ball moving along the blue arrow. Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Generate the motion the orange gear rotates counterclockwise. Static shot, with no zoom or pan.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Generate the motion orange gear begins to rotate clockwise. The camera stays static, with no zoom or pan.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
The upper string breaks. Then, show the trajectory of both objects. Ensure the motion is physically accurate. Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Release the ball and show its motion. The curve has friction. Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Animate the motion of releasing both blocks. Static camera.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Let both objects move toward each other at their given speeds on a frictionless surface and collide elastically. Show the full motion and update their velocities instantly at impact.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
A simple line-art toy car accelerates from rest along a horizontal ruler on a plain white background. It moves right, starting slow and getting progressively faster. Dashed ghost outlines mark its position at the next interval at .5s. Static Shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Two rectangular blocks on a flat surface against a plain white background. The left block, labeled "M," moves right with velocity v, and the right block, labeled "2M," moves left with velocity v. Arrows above each block indicate their opposite directions. The blocks slide toward each other and collide at the center, st...
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
A 2 kg rectangular block is placed on a 30° incline against a plain white background. The block begins sliding down the slope with visible constant acceleration along the incline. Draw an arrow indicating the direction and magnitude of the kinetic friction acting on the block. Static shot with smooth, continuous motion...
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
A rectangular block labeled "W" rests on a horizontal surface against a plain white background. A force arrow labeled "F" pulls from the upper-right corner at an angle θ above the horizontal, with a dashed reference line. Generate an arrow indicating the normal force in the image, with value labeled either "<W", "=W" o...
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
A light ray travels through three vertical regions labeled 1, 2, and 3, with indices of refraction n1, n2, and n3, respectively. Given that n2>n1=n3. Generate the motion of the ray which enters region 1 from the lower left at a steep angle then exiting from region 3. Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

Query:
Human hands put together two magnets. Dynamically depict the force between magnets, paying attention to speed and intensity. Static shot.
You are given a query initial frame.

Generate a physically plausible continuation of the query image.

End of preview.

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.

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