Datasets:

PhysInOneP02
/

dataset_card_test

@@ -1,42 +1,42 @@
----
-pretty_name: PhysInOne
-language:
-  - en
-license: cc
-size_categories:
-  - 1M<n<10M
-task_categories:
-  - text-to-video
-  - image-to-video
-  - video-to-video
-  - depth-estimation
-  - image-segmentation
-  - object-detection
-tags:
-  - video
-  - 3d
-  - synthetic-data
-  - physical-reasoning
-  - visual-physics
-  - world-model
-  - video-generation
-  - future-frame-prediction
-  - physical-property-estimation
-  - motion-transfer
-  - multiview
-  - simulation
-  - embodied-ai
-  - mechanics
-  - optics
-  - fluid-dynamics
-  - magnetism
-  - unreal-engine
-  - mpm
-  - sph
-arxiv: 2604.09415
-homepage: https://vlar-group.github.io/PhysInOne.html
----
 <h1 align="center">PhysInOne: Visual Physics Learning and Reasoning in One Suite</h1>
 <p align="center">
@@ -71,7 +71,10 @@ homepage: https://vlar-group.github.io/PhysInOne.html
 <p align="center">
   <sup>†</sup> Equal Contribution
   <sup>‡</sup> Corresponding Author
-  <sup>*</sup> Data Team
 </p>
 <p align="center">
@@ -94,577 +97,577 @@ homepage: https://vlar-group.github.io/PhysInOne.html
   <a href="TODO">
     <img src="https://img.shields.io/badge/Leaderboard-Coming_Soon-blue">
   </a>
-</p>
-### Summary
-**PhysInOne** is a large-scale synthetic dataset for visual physics learning and reasoning. It contains **153,810 dynamic 3D scenes** and **2 million annotated videos**, covering **71 basic physical phenomena** across four domains of everyday physics: **mechanics**, **optics**, **fluid dynamics**, and **magnetism**.
-Each scene may contain multi-object and multi-physics interactions in complex 3D environments. PhysInOne provides rich annotations including RGB videos, depth maps, object masks, 3D trajectories, camera poses, object meshes, material properties, and textual descriptions.
-The dataset is designed to support research on physics-aware video generation, future frame prediction, physical property estimation, motion transfer, physical reasoning, and world models.
-### News and Release Timetable
-| Component                       | Status                    | Expected Release |
-| ------------------------------- | ------------------------- | ---------------- |
-| Rendered data                   | Partially released / TODO | TODO             |
-| Rendered data: train split      | TODO                      | TODO             |
-| Rendered data: test split       | TODO                      | TODO             |
-| Rendered data: validation split | TODO                      | TODO             |
-| Train split update              | TODO                      | May 21           |
-| 3D assets                       | Not yet released          | Around June      |
-| Leaderboard                     | Ongoing                   | Link TODO        |
-| Baseline code                   | Not yet released          | Around June      |
-| Data processing code            | Not yet released          | Around June      |
-### Visual Overview
-<p align="center">
-  <img src="./assets/teaser.jpg" width="900">
-</p>
-<p align="center">
-  <em>PhysInOne covers dynamic 3D physical scenes across mechanics, fluid dynamics, optics, and magnetism.</em>
-</p>
-### Video Demo Gallery
-We provide lightweight GIF previews in this README and high-quality MP4 examples as separate files.
-| Mechanics                            | Fluid Dynamics                   |
-| ------------------------------------ | -------------------------------- |
-| ![](./assets/examples_mechanics.gif) | ![](./assets/examples_fluid.gif) |
-| Optics                            | Magnetism                            |
-| --------------------------------- | ------------------------------------ |
-| ![](./assets/examples_optics.gif) | ![](./assets/examples_magnetism.gif) |
-| Domain         | High-quality MP4                              |
-| -------------- | --------------------------------------------- |
-| Mechanics      | [View video](./assets/examples_mechanics.mp4) |
-| Fluid Dynamics | [View video](./assets/examples_fluid.mp4)     |
-| Optics         | [View video](./assets/examples_optics.mp4)    |
-| Magnetism      | [View video](./assets/examples_magnetism.mp4) |
-### Dataset Structure
-The exact released structure may vary depending on the hosted version. A recommended structure is shown below.
-```text
-PhysInOne/
-├── README.md
-├── assets/
-│   ├── teaser.jpg
-│   ├── annotation_overview.jpg
-│   ├── examples_mechanics.gif
-│   ├── examples_fluid.gif
-│   ├── examples_optics.gif
-│   ├── examples_magnetism.gif
-│   ├── examples_mechanics.mp4
-│   ├── examples_fluid.mp4
-│   ├── examples_optics.mp4
-│   └── examples_magnetism.mp4
-├── metadata/
-│   ├── train.jsonl
-│   ├── val.jsonl
-│   ├── test.jsonl
-│   ├── benchmark_subsets.json
-│   ├── phenomena_taxonomy.json
-│   └── material_properties.json
-├── videos/
-│   ├── train/
-│   ├── val/
-│   └── test/
-├── annotations/
-│   ├── depth/
-│   ├── segmentation/
-│   ├── captions/
-│   ├── trajectories/
-│   ├── cameras/
-│   └── pointclouds/
-└── scripts/
-    ├── download.py
-    ├── load_sample.py
-    └── visualize_sample.py
-```
-A typical metadata item may look like:
-```json
-{
-  "id": "scene_000000",
-  "scene_name": "TODO",
-  "split": "train",
-  "activity_type": "single",
-  "physical_domains": ["mechanics"],
-  "physical_phenomena": ["gravity", "collision"],
-  "caption_path": "annotations/captions/train/scene_000000/caption.txt",
-  "ue_path": "TODO/scene_000000",
-  "repo_link": "TODO",
-  "download_link": "TODO",
-  "videos": {
-    "fixed_camera_00": "videos/train/scene_000000/camera_00.mp4",
-    "fixed_camera_01": "videos/train/scene_000000/camera_01.mp4",
-    "moving_camera": "videos/train/scene_000000/moving_camera.mp4"
-  },
-  "annotations": {
-    "depth": "annotations/depth/train/scene_000000/",
-    "segmentation": "annotations/segmentation/train/scene_000000/",
-    "caption": "annotations/captions/train/scene_000000/caption.txt",
-    "trajectory": "annotations/trajectories/train/scene_000000/trajectory.json",
-    "camera": "annotations/cameras/train/scene_000000/camera.json",
-    "pointcloud": "annotations/pointclouds/train/scene_000000/points.ply"
-  }
-}
-```
-### Data Splits
-PhysInOne is split into train, validation, and test sets. Each split is intended for a different stage of model development and evaluation.
-| Split      | Purpose                      | Description                                                    |
-| ---------- | ---------------------------- | -------------------------------------------------------------- |
-| Train      | Training and fine-tuning     | Used for learning from rendered dynamic scenes and annotations |
-| Validation | Model selection and ablation | Used for validation and development-time evaluation            |
-| Test       | Final evaluation             | Used for held-out benchmarking and leaderboard submission      |
-The split metadata files are expected to be provided as:
-```text
-metadata/train.jsonl
-metadata/val.jsonl
-metadata/test.jsonl
-```
-Each JSONL entry should contain the scene identifier, scene name, physical category labels, file paths, and download links.
-## Dataset Viewer
-The Hugging Face Dataset Viewer is designed to help users quickly search, filter, and export scene-level metadata without downloading the full dataset.
-### Filtering and Search
-The viewer should support filtering by:
-- **Activity complexity:** `single`, `double`, `triple`
-- **Physical domain:** `mechanics`, `fluid_dynamics`, `optics`, `magnetism`
-- **Keyword search:** scene name, physical phenomenon, caption keyword, object keyword, or material keyword
-- **Split:** `train`, `val`, `test`
-- **Availability:** rendered data, 3D assets, annotations, benchmark subset
-### Viewer Table
-After selecting filters, the viewer should display a dataframe-like table with at least the following columns:
-| Column            | Description                                                   |
-| ----------------- | ------------------------------------------------------------- |
-| `id`              | Unique scene identifier                                       |
-| `scene_name`      | Human-readable scene name                                     |
-| `split`           | Train / validation / test split                               |
-| `activity_type`   | Single-, double-, or triple-physics activity                  |
-| `physical_domain` | Mechanics, fluid dynamics, optics, or magnetism               |
-| `phenomena`       | Physical phenomena involved in the scene                      |
-| `ue_path`         | Unreal Engine scene or asset path                             |
-| `repo_link`       | Link to the corresponding repository item or hosted data page |
-| `download_link`   | Direct download link for the scene package or rendered data   |
-### JSON Export
-The viewer should provide an **Export JSON** button. The exported JSON should contain selected scenes and their download links.
-Example export format:
-```json
-{
-  "selected_scenes": [
-    {
-      "id": "scene_000000",
-      "scene_name": "TODO",
-      "download_link": "TODO"
-    },
-    {
-      "id": "scene_000001",
-      "scene_name": "TODO",
-      "download_link": "TODO"
-    }
-  ]
-}
-```
-This JSON file can be passed directly to the download script.
-## How to Use
-### Install Dependencies
-```bash
-pip install datasets huggingface_hub pandas tqdm
-```
-### Download Metadata Only
-```bash
-huggingface-cli download TODO/PhysInOne \
-  --include "metadata/*" \
-  --local-dir ./PhysInOne
-```
-### Download by Exported JSON
-After selecting scenes in the Dataset Viewer, export the selected scene list as JSON and download the corresponding files:
-```bash
-python scripts/download.py \
-  --selection selected_scenes.json \
-  --output_dir ./PhysInOne
-```
-### Download a Split
-```bash
-python scripts/download.py \
-  --split train \
-  --output_dir ./PhysInOne
-```
-```bash
-python scripts/download.py \
-  --split val \
-  --output_dir ./PhysInOne
-```
-```bash
-python scripts/download.py \
-  --split test \
-  --output_dir ./PhysInOne
-```
-### Load Metadata
-```python
-import json
-from pathlib import Path
-metadata_path = Path("./PhysInOne/metadata/train.jsonl")
-with open(metadata_path, "r") as f:
-    sample = json.loads(next(f))
-print("ID:", sample["id"])
-print("Scene name:", sample["scene_name"])
-print("Split:", sample["split"])
-print("Physical domains:", sample["physical_domains"])
-print("Phenomena:", sample["physical_phenomena"])
-print("Download link:", sample["download_link"])
-```
-### Visualize a Scene
-```bash
-python scripts/visualize_sample.py \
-  --scene_id scene_000000 \
-  --data_root ./PhysInOne
-```
-## Benchmark Subsets
-We provide mini benchmark subsets for lightweight evaluation and quick prototyping.
-| Subset                | Size                  | Intended Use                                     |
-| --------------------- | ---------------------:| ------------------------------------------------ |
-| `test-small`          | 772 text-video pairs  | Physics-aware video generation                   |
-| `test-mini`           | 103 scenes            | Long-term and short-term future frame prediction |
-| `test-tiny`           | 20 scenes             | Physical property estimation                     |
-| `motion-transfer-val` | 273 validation scenes | Motion transfer evaluation                       |
-The benchmark subset metadata is expected to be stored in:
-```text
-metadata/benchmark_subsets.json
-```
-Example format:
-```json
-{
-  "test-small": ["scene_000000", "scene_000001"],
-  "test-mini": ["scene_000100", "scene_000101"],
-  "test-tiny": ["scene_000200", "scene_000201"],
-  "motion-transfer-val": ["scene_000300", "scene_000301"]
-}
-```
-## Data Fields
-### Abbreviations
-| Abbreviation | Meaning                                     |
-| ------------ | ------------------------------------------- |
-| `id`         | Unique scene identifier                     |
-| `ue_path`    | Unreal Engine project, scene, or asset path |
-| `repo_link`  | Link to the corresponding hosted repo item  |
-| `rgb`        | Rendered RGB video                          |
-| `depth`      | Ground-truth depth                          |
-| `seg`        | Segmentation mask                           |
-| `traj`       | Object trajectory                           |
-| `cam`        | Camera metadata                             |
-| `pc`         | Point cloud                                 |
-### Core Fields
-| Field                | Type         | Description                                                                      |
-| -------------------- | ------------ | -------------------------------------------------------------------------------- |
-| `id`                 | string       | Unique scene identifier                                                          |
-| `scene_name`         | string       | Human-readable scene name                                                        |
-| `split`              | string       | `train`, `val`, or `test`                                                        |
-| `activity_type`      | string       | `single`, `double`, or `triple`                                                  |
-| `physical_domains`   | list[string] | One or more of mechanics, fluid dynamics, optics, and magnetism                  |
-| `physical_phenomena` | list[string] | Physical phenomena involved in the scene                                         |
-| `caption_path`       | string       | Path to `caption.txt`                                                            |
-| `ue_path`            | string       | Unreal Engine scene or asset path                                                |
-| `repo_link`          | string       | Hosted repository link                                                           |
-| `download_link`      | string       | Scene-level or package-level download link                                       |
-| `videos`             | dict         | Paths to fixed-camera and moving-camera videos                                   |
-| `annotations`        | dict         | Paths to depth, segmentation, caption, trajectory, camera, and point cloud files |
-## Annotation Details
-PhysInOne provides synchronized visual and physical annotations for each dynamic 3D scene.
-<p align="center">
-  <img src="./assets/annotation_overview.jpg" width="900">
-</p>
-### Depth
-Depth maps are synchronized with RGB frames.
-Please specify the following in the final release:
-- Depth unit: TODO, for example meter or Unreal Engine unit.
-- Depth convention: TODO, for example camera-space z-depth or Euclidean distance.
-- File format: TODO, for example PNG, EXR, NPY, or NPZ.
-- Value range and invalid value convention: TODO.
-### Segmentation
-Segmentation masks encode background, static foreground objects, and dynamic foreground objects.
-Expected encoding:
-| Pixel Value | Meaning                    |
-| -----------:| -------------------------- |
-| `0`         | Background                 |
-| `1-127`     | Static foreground objects  |
-| `128-255`   | Dynamic foreground objects |
-Please specify whether the segmentation is semantic-level, instance-level, or mixed.
-### Captions
-Each scene includes a `caption.txt` file containing an English paragraph that describes the visual elements and the physical activity.
-Expected path format:
-```text
-annotations/captions/{split}/{scene_id}/caption.txt
-```
-### Trajectories
-Each scene includes a `trajectory.json` file storing object trajectory data.
-Recommended contents:
-```json
-{
-  "scene_id": "scene_000000",
-  "fps": 30,
-  "objects": [
-    {
-      "object_id": "object_000",
-      "object_name": "TODO",
-      "is_dynamic": true,
-      "positions": [[0.0, 0.0, 0.0]],
-      "rotations": [[1.0, 0.0, 0.0, 0.0]],
-      "timestamps": [0.0]
-    }
-  ]
-}
-```
-Please specify the coordinate system, units, rotation convention, and timestamp convention in the final release.
-### Cameras
-Each scene includes a camera JSON file describing fixed cameras and the moving monocular camera.
-Recommended contents:
-```json
-{
-  "scene_id": "scene_000000",
-  "cameras": [
-    {
-      "camera_id": "camera_00",
-      "type": "fixed",
-      "intrinsics": {
-        "fx": 0.0,
-        "fy": 0.0,
-        "cx": 0.0,
-        "cy": 0.0,
-        "width": 1120,
-        "height": 1120
-      },
-      "extrinsics": {
-        "world_to_camera": [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
-      }
-    }
-  ]
-}
-```
-Please specify whether camera extrinsics are stored as world-to-camera or camera-to-world matrices.
-### Point Clouds
-Each scene may include `points.ply`.
-Expected behavior:
-- The point cloud is sampled from the first frame.
-- It includes multiple camera views after depth back-projection.
-- It is randomly sampled to approximately **100,000 points**.
-- The file format is `.ply`.
-Recommended path format:
-```text
-annotations/pointclouds/{split}/{scene_id}/points.ply
-```
-Please specify whether point colors, normals, semantic labels, or instance labels are included in the `.ply` file.
-## Supported Tasks and Benchmarks
-PhysInOne supports the following visual physics learning and reasoning tasks.
-### Physics-aware Video Generation
-Given text prompts, image conditions, or initial frames, models generate videos that should be visually realistic and physically plausible.
-Representative settings:
-- Text-to-video generation
-- Image-to-video generation
-- Text-image-to-video generation
-- Video model fine-tuning with physics-rich data
-Suggested metrics:
-- PMF: Physical Motion Fidelity
-- FVD
-- Human physical plausibility rating
-### Long-term Future Frame Prediction
-Given the first half of a dynamic scene, models predict the second half of the video.
-Representative settings:
-- Seen-view prediction
-- Novel-view prediction
-- Scene-specific 4D modeling
-- Video prediction
-Suggested metrics:
-- PMF
-- PSNR
-- SSIM
-- LPIPS
-### Continuous Short-term Future Frame Prediction
-Given streaming observations, models continuously predict the next few frames.
-This setting is useful for:
-- Future-aware robot planning
-- Embodied AI
-- Short-horizon physical prediction
-- Dynamic scene understanding
-Suggested metrics:
-- PMF
-- PSNR
-- SSIM
-- LPIPS
-### Physical Property Estimation
-Given visual observations, models estimate physical properties of scene objects and materials.
-Example target properties:
-- Young's modulus
-- Poisson's ratio
-- Viscosity
-- Bulk modulus
-- Yield stress
-- Friction angle
-- Initial velocity
-### Motion Transfer
-Given a source video and a target image or target scene, models transfer physically meaningful motion patterns while preserving the target appearance.
-Suggested metrics:
-- PMF
-- PSNR
-- SSIM
-- LPIPS
-## License
-TODO: Add the final dataset license.
-Please verify the license terms for:
-- Rendered RGB videos
-- Annotations
-- 3D assets
-- Materials
-- Backgrounds
-- Code
-- Benchmark metadata
-If third-party assets have separate licenses, please document them clearly.
-## Citation
-If you use PhysInOne in your research, please cite:
-```bibtex
-@article{zhou2026physinone,
-  title={PhysInOne: Visual Physics Learning and Reasoning in One Suite},
-  author={Zhou, Siyuan and Wang, Hejun and Cheng, Hu and Li, Jinxi and Wang, Dongsheng and Jiang, Junwei and Jin, Yixiao and Huang, Jiayue and Mao, Shiwei and Liu, Shangjia and Yang, Yafei and Song, Hongkang and Wei, Shenxing and Zhang, Zihui and Wang, Bing and Wang, Zhihua and Zou, Chuhang and Yang, Bo},
-  journal={arXiv preprint arXiv:2604.09415},
-  year={2026}
-}
-```
-## Contact
-For questions about the dataset, please contact:
-- TODO: contact email
-- Project page: https://vlar-group.github.io/PhysInOne.html
-- Hugging Face dataset page: TODO
-## Acknowledgements
-This dataset was created by the vLAR Group and collaborators. Please refer to the paper for the full author list and acknowledgements.

+---
+pretty_name: PhysInOne
+language:
+  - en
+license: cc
+size_categories:
+  - 1M<n<10M
+task_categories:
+  - text-to-video
+  - image-to-video
+  - video-to-video
+  - depth-estimation
+  - image-segmentation
+  - object-detection
+tags:
+  - video
+  - 3d
+  - synthetic-data
+  - physical-reasoning
+  - visual-physics
+  - world-model
+  - video-generation
+  - future-frame-prediction
+  - physical-property-estimation
+  - motion-transfer
+  - multiview
+  - simulation
+  - embodied-ai
+  - mechanics
+  - optics
+  - fluid-dynamics
+  - magnetism
+  - unreal-engine
+  - mpm
+  - sph
+arxiv: 2604.09415
+homepage: https://vlar-group.github.io/PhysInOne.html
+---
 <h1 align="center">PhysInOne: Visual Physics Learning and Reasoning in One Suite</h1>
 <p align="center">
 <p align="center">
   <sup>†</sup> Equal Contribution
   <sup>‡</sup> Corresponding Author
+</p>
+<p align="center">
+  <sup>*</sup> {Peng Huang, Shijie Liu, Zhengli Hao, Hao Li, Yitian Li, Wenqi Zhou, Zhihan Zhao, Zongqi He, Hongtao Wen, Shouwang Huang, Peng Yun, Bowen Cheng, Pok Kazaf Fu, Wai Kit Lai, Jiahao Chen, Kaiyuan Wang, Zhixuan Sun, Ziqi Li, Haochen Hu, Di Zhang, Chun Ho Yuen}
 </p>
 <p align="center">
   <a href="TODO">
     <img src="https://img.shields.io/badge/Leaderboard-Coming_Soon-blue">
   </a>
+</p>
+### Summary
+**PhysInOne** is a large-scale synthetic dataset for visual physics learning and reasoning. It contains **153,810 dynamic 3D scenes** and **2 million annotated videos**, covering **71 basic physical phenomena** across four domains of everyday physics: **mechanics**, **optics**, **fluid dynamics**, and **magnetism**.
+Each scene may contain multi-object and multi-physics interactions in complex 3D environments. PhysInOne provides rich annotations including RGB videos, depth maps, object masks, 3D trajectories, camera poses, object meshes, material properties, and textual descriptions.
+The dataset is designed to support research on physics-aware video generation, future frame prediction, physical property estimation, motion transfer, physical reasoning, and world models.
+### News and Release Timetable
+| Component                       | Status                    | Expected Release |
+| ------------------------------- | ------------------------- | ---------------- |
+| Rendered data                   | Partially released / TODO | TODO             |
+| Rendered data: train split      | TODO                      | TODO             |
+| Rendered data: test split       | TODO                      | TODO             |
+| Rendered data: validation split | TODO                      | TODO             |
+| Train split update              | TODO                      | May 21           |
+| 3D assets                       | Not yet released          | Around June      |
+| Leaderboard                     | Ongoing                   | Link TODO        |
+| Baseline code                   | Not yet released          | Around June      |
+| Data processing code            | Not yet released          | Around June      |
+### Visual Overview
+<p align="center">
+  <img src="./assets/teaser.jpg" width="900">
+</p>
+<p align="center">
+  <em>PhysInOne covers dynamic 3D physical scenes across mechanics, fluid dynamics, optics, and magnetism.</em>
+</p>
+### Video Demo Gallery
+We provide lightweight GIF previews in this README and high-quality MP4 examples as separate files.
+| Mechanics                            | Fluid Dynamics                   |
+| ------------------------------------ | -------------------------------- |
+| ![](./assets/examples_mechanics.gif) | ![](./assets/examples_fluid.gif) |
+| Optics                            | Magnetism                            |
+| --------------------------------- | ------------------------------------ |
+| ![](./assets/examples_optics.gif) | ![](./assets/examples_magnetism.gif) |
+| Domain         | High-quality MP4                              |
+| -------------- | --------------------------------------------- |
+| Mechanics      | [View video](./assets/examples_mechanics.mp4) |
+| Fluid Dynamics | [View video](./assets/examples_fluid.mp4)     |
+| Optics         | [View video](./assets/examples_optics.mp4)    |
+| Magnetism      | [View video](./assets/examples_magnetism.mp4) |
+### Dataset Structure
+The exact released structure may vary depending on the hosted version. A recommended structure is shown below.
+```text
+PhysInOne/
+├── README.md
+├── assets/
+│   ├── teaser.jpg
+│   ├── annotation_overview.jpg
+│   ├── examples_mechanics.gif
+│   ├── examples_fluid.gif
+│   ├── examples_optics.gif
+│   ├── examples_magnetism.gif
+│   ├── examples_mechanics.mp4
+│   ├── examples_fluid.mp4
+│   ├── examples_optics.mp4
+│   └── examples_magnetism.mp4
+├── metadata/
+│   ├── train.jsonl
+│   ├── val.jsonl
+│   ├── test.jsonl
+│   ├── benchmark_subsets.json
+│   ├── phenomena_taxonomy.json
+│   └── material_properties.json
+├── videos/
+│   ├── train/
+│   ├── val/
+│   └── test/
+├── annotations/
+│   ├── depth/
+│   ├── segmentation/
+│   ├── captions/
+│   ├── trajectories/
+│   ├── cameras/
+│   └── pointclouds/
+└── scripts/
+    ├── download.py
+    ├── load_sample.py
+    └── visualize_sample.py
+```
+A typical metadata item may look like:
+```json
+{
+  "id": "scene_000000",
+  "scene_name": "TODO",
+  "split": "train",
+  "activity_type": "single",
+  "physical_domains": ["mechanics"],
+  "physical_phenomena": ["gravity", "collision"],
+  "caption_path": "annotations/captions/train/scene_000000/caption.txt",
+  "ue_path": "TODO/scene_000000",
+  "repo_link": "TODO",
+  "download_link": "TODO",
+  "videos": {
+    "fixed_camera_00": "videos/train/scene_000000/camera_00.mp4",
+    "fixed_camera_01": "videos/train/scene_000000/camera_01.mp4",
+    "moving_camera": "videos/train/scene_000000/moving_camera.mp4"
+  },
+  "annotations": {
+    "depth": "annotations/depth/train/scene_000000/",
+    "segmentation": "annotations/segmentation/train/scene_000000/",
+    "caption": "annotations/captions/train/scene_000000/caption.txt",
+    "trajectory": "annotations/trajectories/train/scene_000000/trajectory.json",
+    "camera": "annotations/cameras/train/scene_000000/camera.json",
+    "pointcloud": "annotations/pointclouds/train/scene_000000/points.ply"
+  }
+}
+```
+### Data Splits
+PhysInOne is split into train, validation, and test sets. Each split is intended for a different stage of model development and evaluation.
+| Split      | Purpose                      | Description                                                    |
+| ---------- | ---------------------------- | -------------------------------------------------------------- |
+| Train      | Training and fine-tuning     | Used for learning from rendered dynamic scenes and annotations |
+| Validation | Model selection and ablation | Used for validation and development-time evaluation            |
+| Test       | Final evaluation             | Used for held-out benchmarking and leaderboard submission      |
+The split metadata files are expected to be provided as:
+```text
+metadata/train.jsonl
+metadata/val.jsonl
+metadata/test.jsonl
+```
+Each JSONL entry should contain the scene identifier, scene name, physical category labels, file paths, and download links.
+## Dataset Viewer
+The Hugging Face Dataset Viewer is designed to help users quickly search, filter, and export scene-level metadata without downloading the full dataset.
+### Filtering and Search
+The viewer should support filtering by:
+- **Activity complexity:** `single`, `double`, `triple`
+- **Physical domain:** `mechanics`, `fluid_dynamics`, `optics`, `magnetism`
+- **Keyword search:** scene name, physical phenomenon, caption keyword, object keyword, or material keyword
+- **Split:** `train`, `val`, `test`
+- **Availability:** rendered data, 3D assets, annotations, benchmark subset
+### Viewer Table
+After selecting filters, the viewer should display a dataframe-like table with at least the following columns:
+| Column            | Description                                                   |
+| ----------------- | ------------------------------------------------------------- |
+| `id`              | Unique scene identifier                                       |
+| `scene_name`      | Human-readable scene name                                     |
+| `split`           | Train / validation / test split                               |
+| `activity_type`   | Single-, double-, or triple-physics activity                  |
+| `physical_domain` | Mechanics, fluid dynamics, optics, or magnetism               |
+| `phenomena`       | Physical phenomena involved in the scene                      |
+| `ue_path`         | Unreal Engine scene or asset path                             |
+| `repo_link`       | Link to the corresponding repository item or hosted data page |
+| `download_link`   | Direct download link for the scene package or rendered data   |
+### JSON Export
+The viewer should provide an **Export JSON** button. The exported JSON should contain selected scenes and their download links.
+Example export format:
+```json
+{
+  "selected_scenes": [
+    {
+      "id": "scene_000000",
+      "scene_name": "TODO",
+      "download_link": "TODO"
+    },
+    {
+      "id": "scene_000001",
+      "scene_name": "TODO",
+      "download_link": "TODO"
+    }
+  ]
+}
+```
+This JSON file can be passed directly to the download script.
+## How to Use
+### Install Dependencies
+```bash
+pip install datasets huggingface_hub pandas tqdm
+```
+### Download Metadata Only
+```bash
+huggingface-cli download TODO/PhysInOne \
+  --include "metadata/*" \
+  --local-dir ./PhysInOne
+```
+### Download by Exported JSON
+After selecting scenes in the Dataset Viewer, export the selected scene list as JSON and download the corresponding files:
+```bash
+python scripts/download.py \
+  --selection selected_scenes.json \
+  --output_dir ./PhysInOne
+```
+### Download a Split
+```bash
+python scripts/download.py \
+  --split train \
+  --output_dir ./PhysInOne
+```
+```bash
+python scripts/download.py \
+  --split val \
+  --output_dir ./PhysInOne
+```
+```bash
+python scripts/download.py \
+  --split test \
+  --output_dir ./PhysInOne
+```
+### Load Metadata
+```python
+import json
+from pathlib import Path
+metadata_path = Path("./PhysInOne/metadata/train.jsonl")
+with open(metadata_path, "r") as f:
+    sample = json.loads(next(f))
+print("ID:", sample["id"])
+print("Scene name:", sample["scene_name"])
+print("Split:", sample["split"])
+print("Physical domains:", sample["physical_domains"])
+print("Phenomena:", sample["physical_phenomena"])
+print("Download link:", sample["download_link"])
+```
+### Visualize a Scene
+```bash
+python scripts/visualize_sample.py \
+  --scene_id scene_000000 \
+  --data_root ./PhysInOne
+```
+## Benchmark Subsets
+We provide mini benchmark subsets for lightweight evaluation and quick prototyping.
+| Subset                | Size                  | Intended Use                                     |
+| --------------------- | ---------------------:| ------------------------------------------------ |
+| `test-small`          | 772 text-video pairs  | Physics-aware video generation                   |
+| `test-mini`           | 103 scenes            | Long-term and short-term future frame prediction |
+| `test-tiny`           | 20 scenes             | Physical property estimation                     |
+| `motion-transfer-val` | 273 validation scenes | Motion transfer evaluation                       |
+The benchmark subset metadata is expected to be stored in:
+```text
+metadata/benchmark_subsets.json
+```
+Example format:
+```json
+{
+  "test-small": ["scene_000000", "scene_000001"],
+  "test-mini": ["scene_000100", "scene_000101"],
+  "test-tiny": ["scene_000200", "scene_000201"],
+  "motion-transfer-val": ["scene_000300", "scene_000301"]
+}
+```
+## Data Fields
+### Abbreviations
+| Abbreviation | Meaning                                     |
+| ------------ | ------------------------------------------- |
+| `id`         | Unique scene identifier                     |
+| `ue_path`    | Unreal Engine project, scene, or asset path |
+| `repo_link`  | Link to the corresponding hosted repo item  |
+| `rgb`        | Rendered RGB video                          |
+| `depth`      | Ground-truth depth                          |
+| `seg`        | Segmentation mask                           |
+| `traj`       | Object trajectory                           |
+| `cam`        | Camera metadata                             |
+| `pc`         | Point cloud                                 |
+### Core Fields
+| Field                | Type         | Description                                                                      |
+| -------------------- | ------------ | -------------------------------------------------------------------------------- |
+| `id`                 | string       | Unique scene identifier                                                          |
+| `scene_name`         | string       | Human-readable scene name                                                        |
+| `split`              | string       | `train`, `val`, or `test`                                                        |
+| `activity_type`      | string       | `single`, `double`, or `triple`                                                  |
+| `physical_domains`   | list[string] | One or more of mechanics, fluid dynamics, optics, and magnetism                  |
+| `physical_phenomena` | list[string] | Physical phenomena involved in the scene                                         |
+| `caption_path`       | string       | Path to `caption.txt`                                                            |
+| `ue_path`            | string       | Unreal Engine scene or asset path                                                |
+| `repo_link`          | string       | Hosted repository link                                                           |
+| `download_link`      | string       | Scene-level or package-level download link                                       |
+| `videos`             | dict         | Paths to fixed-camera and moving-camera videos                                   |
+| `annotations`        | dict         | Paths to depth, segmentation, caption, trajectory, camera, and point cloud files |
+## Annotation Details
+PhysInOne provides synchronized visual and physical annotations for each dynamic 3D scene.
+<p align="center">
+  <img src="./assets/annotation_overview.jpg" width="900">
+</p>
+### Depth
+Depth maps are synchronized with RGB frames.
+Please specify the following in the final release:
+- Depth unit: TODO, for example meter or Unreal Engine unit.
+- Depth convention: TODO, for example camera-space z-depth or Euclidean distance.
+- File format: TODO, for example PNG, EXR, NPY, or NPZ.
+- Value range and invalid value convention: TODO.
+### Segmentation
+Segmentation masks encode background, static foreground objects, and dynamic foreground objects.
+Expected encoding:
+| Pixel Value | Meaning                    |
+| -----------:| -------------------------- |
+| `0`         | Background                 |
+| `1-127`     | Static foreground objects  |
+| `128-255`   | Dynamic foreground objects |
+Please specify whether the segmentation is semantic-level, instance-level, or mixed.
+### Captions
+Each scene includes a `caption.txt` file containing an English paragraph that describes the visual elements and the physical activity.
+Expected path format:
+```text
+annotations/captions/{split}/{scene_id}/caption.txt
+```
+### Trajectories
+Each scene includes a `trajectory.json` file storing object trajectory data.
+Recommended contents:
+```json
+{
+  "scene_id": "scene_000000",
+  "fps": 30,
+  "objects": [
+    {
+      "object_id": "object_000",
+      "object_name": "TODO",
+      "is_dynamic": true,
+      "positions": [[0.0, 0.0, 0.0]],
+      "rotations": [[1.0, 0.0, 0.0, 0.0]],
+      "timestamps": [0.0]
+    }
+  ]
+}
+```
+Please specify the coordinate system, units, rotation convention, and timestamp convention in the final release.
+### Cameras
+Each scene includes a camera JSON file describing fixed cameras and the moving monocular camera.
+Recommended contents:
+```json
+{
+  "scene_id": "scene_000000",
+  "cameras": [
+    {
+      "camera_id": "camera_00",
+      "type": "fixed",
+      "intrinsics": {
+        "fx": 0.0,
+        "fy": 0.0,
+        "cx": 0.0,
+        "cy": 0.0,
+        "width": 1120,
+        "height": 1120
+      },
+      "extrinsics": {
+        "world_to_camera": [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
+      }
+    }
+  ]
+}
+```
+Please specify whether camera extrinsics are stored as world-to-camera or camera-to-world matrices.
+### Point Clouds
+Each scene may include `points.ply`.
+Expected behavior:
+- The point cloud is sampled from the first frame.
+- It includes multiple camera views after depth back-projection.
+- It is randomly sampled to approximately **100,000 points**.
+- The file format is `.ply`.
+Recommended path format:
+```text
+annotations/pointclouds/{split}/{scene_id}/points.ply
+```
+Please specify whether point colors, normals, semantic labels, or instance labels are included in the `.ply` file.
+## Supported Tasks and Benchmarks
+PhysInOne supports the following visual physics learning and reasoning tasks.
+### Physics-aware Video Generation
+Given text prompts, image conditions, or initial frames, models generate videos that should be visually realistic and physically plausible.
+Representative settings:
+- Text-to-video generation
+- Image-to-video generation
+- Text-image-to-video generation
+- Video model fine-tuning with physics-rich data
+Suggested metrics:
+- PMF: Physical Motion Fidelity
+- FVD
+- Human physical plausibility rating
+### Long-term Future Frame Prediction
+Given the first half of a dynamic scene, models predict the second half of the video.
+Representative settings:
+- Seen-view prediction
+- Novel-view prediction
+- Scene-specific 4D modeling
+- Video prediction
+Suggested metrics:
+- PMF
+- PSNR
+- SSIM
+- LPIPS
+### Continuous Short-term Future Frame Prediction
+Given streaming observations, models continuously predict the next few frames.
+This setting is useful for:
+- Future-aware robot planning
+- Embodied AI
+- Short-horizon physical prediction
+- Dynamic scene understanding
+Suggested metrics:
+- PMF
+- PSNR
+- SSIM
+- LPIPS
+### Physical Property Estimation
+Given visual observations, models estimate physical properties of scene objects and materials.
+Example target properties:
+- Young's modulus
+- Poisson's ratio
+- Viscosity
+- Bulk modulus
+- Yield stress
+- Friction angle
+- Initial velocity
+### Motion Transfer
+Given a source video and a target image or target scene, models transfer physically meaningful motion patterns while preserving the target appearance.
+Suggested metrics:
+- PMF
+- PSNR
+- SSIM
+- LPIPS
+## License
+TODO: Add the final dataset license.
+Please verify the license terms for:
+- Rendered RGB videos
+- Annotations
+- 3D assets
+- Materials
+- Backgrounds
+- Code
+- Benchmark metadata
+If third-party assets have separate licenses, please document them clearly.
+## Citation
+If you use PhysInOne in your research, please cite:
+```bibtex
+@article{zhou2026physinone,
+  title={PhysInOne: Visual Physics Learning and Reasoning in One Suite},
+  author={Zhou, Siyuan and Wang, Hejun and Cheng, Hu and Li, Jinxi and Wang, Dongsheng and Jiang, Junwei and Jin, Yixiao and Huang, Jiayue and Mao, Shiwei and Liu, Shangjia and Yang, Yafei and Song, Hongkang and Wei, Shenxing and Zhang, Zihui and Wang, Bing and Wang, Zhihua and Zou, Chuhang and Yang, Bo},
+  journal={arXiv preprint arXiv:2604.09415},
+  year={2026}
+}
+```
+## Contact
+For questions about the dataset, please contact:
+- TODO: contact email
+- Project page: https://vlar-group.github.io/PhysInOne.html
+- Hugging Face dataset page: TODO
+## Acknowledgements
+This dataset was created by the vLAR Group and collaborators. Please refer to the paper for the full author list and acknowledgements.