Datasets:

Vincwng
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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
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A grabber arm is holding a tennis ball above a piece of cardstock propped up on a rotating platform sitting on a table that rotates clockwise. The grabber lowers the ball and places is on the table as the cardstock rotates. Static shot with no camera movement.

Physical mistake hint:
A typical bad-physics outcome is that the object visibility is inconsistent with the occluder, appears in front when it should be behind, or disappears unnaturally.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A grabber arm is holding a tennis ball above a piece of cardstock propped up on a rotating platform sitting on a table that rotates clockwise. The grabber lowers the ball and places is on the table as the cardstock rotates. Static shot with no camera movement.

Physical mistake hint:
A typical bad-physics outcome is that the object visibility is inconsistent with the occluder, appears in front when it should be behind, or disappears unnaturally.
A grabber arm is holding a tennis ball above a piece of cardstock propped up on a rotating platform sitting on a table that rotates clockwise. The grabber lowers the ball and places is on the table as the cardstock rotates. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A light-colored wooden coffee table with a few small objects on it including a tennis ball and a smaller red ball. An orange ball rolls out of a black pipe that is sitting on the table towards the right side. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A light-colored wooden coffee table with a few small objects on it including a tennis ball and a smaller red ball. An orange ball rolls out of a black pipe that is sitting on the table towards the right side. Static shot with no camera movement.
A light-colored wooden coffee table with a few small objects on it including a tennis ball and a smaller red ball. An orange ball rolls out of a black pipe that is sitting on the table towards the right side. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
An orange inflatable basketball is suspended above a black plastic crate placed on a wooden table. The ball is then released. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
An orange inflatable basketball is suspended above a black plastic crate placed on a wooden table. The ball is then released. Static shot with no camera movement.
An orange inflatable basketball is suspended above a black plastic crate placed on a wooden table. The ball is then released. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A blue grabber tool holds a tennis ball above a pile of green kinetic sand on a wooden table. The grabber then releases the ball. Static shot with no camera movement.

Physical mistake hint:
A typical bad-physics outcome is that the soft surface does not deform on impact, the object passes through it, or the object rebounds impossibly.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A blue grabber tool holds a tennis ball above a pile of green kinetic sand on a wooden table. The grabber then releases the ball. Static shot with no camera movement.

Physical mistake hint:
A typical bad-physics outcome is that the soft surface does not deform on impact, the object passes through it, or the object rebounds impossibly.
A blue grabber tool holds a tennis ball above a pile of green kinetic sand on a wooden table. The grabber then releases the ball. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A simple ramp made of cardboard propped up by a blue block on a light-colored wooden table. There's a black pipe to the left of the frame and a yellow tennis ball rolls out of the pipe towards the ramp. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A simple ramp made of cardboard propped up by a blue block on a light-colored wooden table. There's a black pipe to the left of the frame and a yellow tennis ball rolls out of the pipe towards the ramp. Static shot with no camera movement.
A simple ramp made of cardboard propped up by a blue block on a light-colored wooden table. There's a black pipe to the left of the frame and a yellow tennis ball rolls out of the pipe towards the ramp. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A light wood coffee table in the foreground with a black pipe on the end of the table. A grey tennis ball rolls out of the pipe towards the right and onto the table. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A light wood coffee table in the foreground with a black pipe on the end of the table. A grey tennis ball rolls out of the pipe towards the right and onto the table. Static shot with no camera movement.
A light wood coffee table in the foreground with a black pipe on the end of the table. A grey tennis ball rolls out of the pipe towards the right and onto the table. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

Query:
A piece of clear glass resting on the edge of a light-colored wooden table against a plain white wall. A blue tennis ball rolls on the wooden table and towards the glass. Static shot with no camera movement.
Generate a visually realistic continuation of the query image, but make the physical interaction clearly incorrect.

The scene, objects, and camera should remain consistent with the query image. The video should not look like a complete visual failure. Instead, it should contain a recognizable physical mistake, such as impossible motion, missing interaction, incorrect collision response, wrong liquid behavior, inconsistent reflection...

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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