# PhysicsMind Video Dataset

This is the video generation (VG) component of the **PhysicsMind** benchmark, a unified physics-grounded benchmark that evaluates both reasoning and prediction under the same physical laws in both simulated and real-world settings.

## About PhysicsMind

PhysicsMind is designed to evaluate whether video generation models can produce physically plausible motion trajectories that obey center-of-mass, torque, and inertial constraints. The benchmark focuses on three canonical mechanics scenarios:

1. **Center of Mass** - Measuring center of gravity by suspension method
2. **Lever Equilibrium** - Lever balance conditions
3. **Newton's First Law** - Inertia verification experiments

Each scenario is realized with diverse real tabletop experiments and 2D simulations, enabling evaluation of whether models trained in simulation environments generalize well under real-world noise, dynamic differences, and visual variability.

## Directory Structure

```
vieo_data/
├── Lever_Balance_Conditions/
│   ├── Boundary_Frames/
│   │   ├── Lever_Balance_Conditions_X_Initial_Frame.png
│   │   └── Lever_Balance_Conditions_X_Final_Frame.png
│   ├── video/
│   │   └── Lever_Balance_Conditions_X.mov
│   ├── data_lever_balance_en.json
│   ├── data_lever_balance_scene_en.json
│   └── data_lever_balance_scene_object_law_en.json
│
├── Verification_of_Newton's First_Law/
│   ├── Boundary_Frames/
│   │   ├── Newtons_First_Law_X_Initial_Frame.png
│   │   └── Newtons_First_Law_X_Final_Frame.png
│   ├── video/
│   │   └── Newtons_First_Law_X.mp4
│   ├── data_newton_first_law_en.json
│   ├── data_newton_first_law_scene_en.json
│   └── data_newton_first_law_scene_action_law_en.json
│
└── Measuring_Center_of_Gravity_by_Suspension_Method/
    ├── real/
    │   ├── Boundary_Frames/
    │   │   ├── Center_of_Gravity_Suspension_X_Initial_Frame.png
    │   │   └── Center_of_Gravity_Suspension_X_Final_Frame.png
    │   ├── video/
    │   │   └── Center_of_Gravity_Suspension_X.mp4
    │   ├── data.json
    │   ├── data_scene.json
    │   └── data_scene_action_law.json
    │
    └── sim/
        ├── Boundary_Frames/
        │   ├── Center_of_Gravity_Suspension_Sim_X_Initial_Frame.png
        │   └── Center_of_Gravity_Suspension_Sim_X_Final_Frame.png
        ├── video/
        │   └── Center_of_Gravity_Suspension_Sim_X.mp4
        ├── data.json
        ├── original_prompts.json
        ├── refined_prompts.json
        └── negative_prompts.json
```

## Experiments

### 1. Lever Balance Conditions
Demonstrates lever equilibrium principles, showing how forces and distances from the fulcrum affect balance. Videos feature varying initial setups and object types.

### 2. Verification of Newton's First Law (Inertia)
Illustrates Newton's First Law of Motion through tablecloth-pull experiments with varying pulling speeds and object masses.

### 3. Measuring Center of Gravity by Suspension Method
Shows how to determine an object's center of gravity using the suspension method with varying object shapes and suspension points.
- **real/**: Real-world tabletop experiment recordings
- **sim/**: 2D simulated videos

## File Descriptions

| File | Description |
|------|-------------|
| `Boundary_Frames/` | Initial and final frame images for each video |
| `video/` | Video files (.mov or .mp4) |
| `data*.json` | Basic metadata with video name, path, category, and prompts |
| `*_scene*.json` | Scene-level descriptions |
| `*_action_law*.json` or `*_object_law*.json` | Physics law and action descriptions |
| `original_prompts.json` | Original text prompts for video generation |
| `refined_prompts.json` | Refined/improved prompts |
| `negative_prompts.json` | Negative prompts for video generation |

## JSON Fields

- **Video Name**: Filename of the video
- **Video Path (Relative)**: Relative path to the video file
- **Category**: Experiment category
- **Initial Frame**: Path to the first frame image
- **Final Frame**: Path to the last frame image
- **Prompt**: Text description of the video content

## Citation

If you use this dataset, please cite the PhysicsMind paper.