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README.md
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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.
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#
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| Component | Progress | Status | Notes |
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| --------------------- | ----------------------------- | ------------ | --------------------------------- |
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| Rendered Data - Train | `█░░░░░░░░░` 5%(5277/122,988) | In progress | Last updated: May 21 |
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| Rendered Data - Test | `░░░░░░░░░░` 0%(0/15411) | In progress | |
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| Rendered Data - Val | `░░░░░░░░░░` 1%(103/15411) | In progress | |
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| Leaderboard | `░░░░░░░░░░` 0% | Ongoing | Link will be added when available |
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| Baseline code | `░░░░░░░░░░` 0% | Not released | Expected around June |
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| Data processing | `░░░░░░░░░░` 0% | Not released | Expected around June |
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| SubSet | `██████████`100% | Released | |
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#
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### Filtering and Search
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--output_dir ./PhysInOne
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```
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### Download
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```bash
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python scripts/download.py \
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--output_dir ./PhysInOne
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```
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#
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<p align="center">
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<img src="assets/teaser.png" width="900">
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</p>
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The exact released structure may vary depending on the hosted version. A recommended structure is shown below.
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└── visualize_sample.py
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```
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#
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PhysInOne is split into train, validation, and test sets. Each split is intended for a different stage of model development and evaluation.
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| Validation | Model selection and ablation | Used for validation and development-time evaluation |
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| Test | Final evaluation | Used for held-out benchmarking and leaderboard submission |
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### Abbreviations
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| ----- | ---- | ----------- |
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| `id` | | |
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PhysInOne provides synchronized visual and physical annotations for each dynamic 3D scene.
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### Point Clouds
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Each scene
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Expected behavior:
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- It includes multiple camera views after depth back-projection.
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- It is randomly sampled to approximately **100,000 points**.
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- The file format is `.ply`.
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Recommended path format:
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Please specify whether point colors, normals, semantic labels, or instance labels are included in the `.ply` file.
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PhysInOne supports the following visual physics learning and reasoning tasks.
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- Motion Transfer
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#
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TODO: Add the final dataset license.
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#
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If you use PhysInOne in your research, please cite:
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}
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```
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#
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For questions about the dataset, please contact:
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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.
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# 🚀 Release Timetable
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| Component | Progress | Status | Notes |
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| --------------------- | ----------------------------- | ------------ | --------------------------------- |
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| SubSet | `██████████`100% | Released | |
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| Rendered Data - Train | `█░░░░░░░░░` 5%(5277/122,988) | In progress | Last updated: May 21 |
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| Rendered Data - Test | `░░░░░░░░░░` 0%(0/15411) | In progress | |
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| Rendered Data - Val | `░░░░░░░░░░` 1%(103/15411) | In progress | |
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| Leaderboard | `░░░░░░░░░░` 0% | Ongoing | Link will be added when available |
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| Baseline code | `░░░░░░░░░░` 0% | Not released | Expected around June |
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| Data processing | `░░░░░░░░░░` 0% | Not released | Expected around June |
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# 📥 How to Use
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### Filtering and Search
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--output_dir ./PhysInOne
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```
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### Download via Filters
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```bash
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python scripts/download.py \
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--output_dir ./PhysInOne
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```
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# 🎬 Visual Overview
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<p align="center">
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<img src="assets/teaser.png" width="900">
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</p>
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# 📦 Dataset Structure
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The exact released structure may vary depending on the hosted version. A recommended structure is shown below.
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└── visualize_sample.py
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```
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# 📊 Data Splits
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PhysInOne is split into train, validation, and test sets. Each split is intended for a different stage of model development and evaluation.
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| Validation | Model selection and ablation | Used for validation and development-time evaluation |
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| Test | Final evaluation | Used for held-out benchmarking and leaderboard submission |
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# 🧩 Data Fields
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### Abbreviations
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| ----- | ---- | ----------- |
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| `id` | | |
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# 📝 Annotation Details
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PhysInOne provides synchronized visual and physical annotations for each dynamic 3D scene.
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### Point Clouds
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Each scene should include `points.ply`.
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The initial point cloud was obtained by back-projecting the pixels from each camera view in the first frame based on depth, followed by randomly sampling 100,000 points from the resulting data.
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Recommended path format:
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Please specify whether point colors, normals, semantic labels, or instance labels are included in the `.ply` file.
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# 🧪 Supported Tasks and Benchmarks
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PhysInOne supports the following visual physics learning and reasoning tasks.
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- Motion Transfer
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# 📜 License
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TODO: Add the final dataset license.
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# 📚 Citation
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If you use PhysInOne in your research, please cite:
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}
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```
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# 📮 Contact
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For questions about the dataset, please contact:
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