Upload PhysInOne - Visual Physics Learning and Reasoning in One Suite.md

PhysInOne - Visual Physics Learning and Reasoning in One Suite.md

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+---
+pretty_name: PhysInOne
+language:
+  - en
+license: TODO
+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
+---
+
+# PhysInOne: Visual Physics Learning and Reasoning in One Suite
+
+[Project Page](https://vlar-group.github.io/PhysInOne.html) | [Paper](https://arxiv.org/abs/2604.09415) | [Code](TODO) | [Dataset](TODO)
+
+## Dataset 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**, systematically covering **71 basic physical phenomena** across four domains of everyday physics: **mechanics**, **optics**, **fluid dynamics**, and **magnetism**.
+
+Unlike prior visual physics datasets that are often limited in scale, physical diversity, or annotation richness, PhysInOne provides multi-object and multi-physics interactions in complex 3D environments, together with comprehensive annotations including **geometry**, **semantics**, **motion**, **physical properties**, and **text descriptions**.
+
+PhysInOne is designed to support research on physics-grounded world models, physically plausible video generation, future frame prediction, physical property estimation, motion transfer, and embodied AI.
+
+## News
+
+- **2026-04-10**: PhysInOne paper released on arXiv.
+- **TODO**: Dataset released on Hugging Face.
+- **TODO**: Benchmark code and evaluation scripts released.
+
+## Dataset Highlights
+
+| Feature                 | Description                                                                                             |
+| ----------------------- | ------------------------------------------------------------------------------------------------------- |
+| Dynamic 3D scenes       | 153,810                                                                                                 |
+| Dynamic videos          | 2 million                                                                                               |
+| Physical phenomena      | 71                                                                                                      |
+| Physical domains        | Mechanics, Optics, Fluid Dynamics, Magnetism                                                            |
+| Multiphysics activities | 3,284                                                                                                   |
+| 3D objects              | 2,231                                                                                                   |
+| Materials               | 623                                                                                                     |
+| 3D backgrounds          | 528                                                                                                     |
+| Cameras per scene       | 12 fixed cameras + 1 moving monocular camera                                                            |
+| Video resolution        | 1120 × 1120                                                                                             |
+| Frame rate              | 30 FPS; 60 FPS for laser-related scenes                                                                 |
+| Average duration        | Approximately 5.2 seconds                                                                               |
+| Annotation types        | RGB, depth, masks, 3D trajectories, object meshes, material properties, camera poses, text descriptions |
+
+## Dataset Description
+
+PhysInOne aims to address the scarcity of large-scale, physically grounded visual data for training and evaluating AI systems. The dataset focuses on visually observable everyday physics and covers four major domains:
+
+- **Mechanics**: gravity, collision, acceleration, equilibrium, rotation, Hooke's law, conservation of momentum, and related phenomena.
+- **Optics**: reflection, refraction, mirror interactions, laser-related activities, and other visible optical phenomena.
+- **Fluid Dynamics**: liquid motion, splashing, buoyancy, droplets, and related interactions.
+- **Magnetism**: magnetic attraction, magnetic imbalance, and multi-object magnetic interactions.
+
+Each scene may involve one or more physical phenomena occurring simultaneously or sequentially. These phenomena are instantiated as concrete 3D scenes with diverse objects, materials, and backgrounds.
+
+## Supported Tasks
+
+PhysInOne can be used for the following research tasks.
+
+### 1. Physics-aware Video Generation
+
+Given text prompts, initial frames, or image conditions, models are expected to generate videos that are not only visually realistic but also physically plausible.
+
+Potential settings include:
+
+- Text-to-video generation
+- Image-to-video generation
+- Text-image-to-video generation
+- Video model fine-tuning with physics-rich data
+
+Suggested evaluation metrics:
+
+- PMF: Physical Motion Fidelity
+- FVD
+- Human preference / human physical plausibility rating
+
+### 2. Future Frame Prediction
+
+Given observed frames from a dynamic scene, models are expected to predict future frames while preserving physically plausible dynamics.
+
+PhysInOne supports both:
+
+- **Long-term future frame prediction**: predicting the second half of a video from the first half.
+- **Continuous short-term future frame prediction**: repeatedly predicting the next few frames from streaming observations.
+
+The dataset supports evaluation from both seen and novel viewpoints.
+
+### 3. Physical Property Estimation
+
+Given visual observations of dynamic scenes, models are expected to infer physical properties such as:
+
+- Young's modulus
+- Poisson's ratio
+- Viscosity
+- Bulk modulus
+- Yield stress
+- Friction angle
+- Initial velocity
+
+This task is useful for inverse physics, system identification, resimulation, editable scene dynamics, and robot interaction.
+
+### 4. Motion Transfer
+
+Given a source video and a target image or scene, models are expected to transfer physically meaningful motion patterns from the source to the target while preserving the target appearance.
+
+This task is especially challenging because PhysInOne contains complex multi-object and multi-physics interactions.
+
+## Dataset Structure
+
+The exact released structure may vary depending on the hosted version. A recommended structure is shown below.
+
+```text
+PhysInOne/
+├── README.md
+├── metadata/
+│   ├── train.jsonl
+│   ├── val.jsonl
+│   ├── test.jsonl
+│   ├── phenomena_taxonomy.json
+│   ├── material_properties.json
+│   └── benchmark_subsets.json
+├── videos/
+│   ├── train/
+│   ├── val/
+│   └── test/
+├── annotations/
+│   ├── depth/
+│   ├── masks/
+│   ├── trajectories/
+│   ├── camera_poses/
+│   ├── meshes/
+│   └── material_properties/
+└── scripts/
+    ├── download.py
+    ├── load_sample.py
+    └── visualize_sample.py