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# 🌱 PhysGaia: A Physics-Aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis (CVPR 2026)

<a href="https://mjmjeong.github.io/">Mijeong Kim</a>* · 
 <a href="https://gunhee1113.github.io/">Gunhee Kim</a>* · 
 Jungyoon Choi · 
 Wonjae Roh · 
 <a href="https://cv.snu.ac.kr/index.php/bhhan/">Bohyung Han</a>

\* Equal Contribution

## 🚀 News
- **[2026-02]** PhysGaia has been accepted to **CVPR 2026**! 🎊
- **[2025-06]** All related code can be found across multiple branches 🔗
- **[2025-05]** Project page and dataset are now available.

## **⭐️ Key Highlights**

- **💥 Multi-body interaction**
- **💎 Various materials** across all modalities
    - Liquid, Gas, Viscoelastic substance, and Textile
- **✏️ Physical evaluation**
    - physics parameters
    - ground-truth 3D trajectories
- **😀 Research friendly!!**
    - Providing codes for recent Dynamic Novel View Synthesis (DyNVS) models
        - [Shape-of-motion](https://github.com/mjmjeong/PhysGaia/tree/SOM), [4DGS](https://github.com/mjmjeong/PhysGaia/tree/4DGS_hex), [STG](https://github.com/mjmjeong/PhysGaia/tree/spacetime), [D-3DGS](https://github.com/mjmjeong/PhysGaia/tree/deformable), [MoSca](https://github.com/JiahuiLei/MoSca)
    - Supporting **diverse training setting**: both monocular & multiview reconstruction

## **🔥 Ideal for “Next” Research**

- 🧠 **Physical reasoning in dynamic scenes**
    - Offering ground-truth physics parameters for precise evaluation of inverse physics estimation
    - Offering ground-truth 3D trajectories for assessing actual motion beyond photorealism.
- 🤝 **Multi-body physical interaction modeling**
- 🧪 **Material-specific physics solver integration**
- 🧬 **Compatibility with existing DyNVS models**

## **📂 Dataset Access**

You can find our full dataset on [**Hugging Face**](https://huggingface.co/datasets/mijeongkim/PhysGaia/tree/main).
Each folder is corresponding to each scene, containing the following files:

```bash
{material_type}_{scene_name}.zip
│
├── render/                              # Rendered images
│   ├── train/                           # Images for training
│   └── test/                            # Images for evaluation
│
├── camera_info_test.json                # Monocular camera info for test
├── camera_info_train_mono.json          # Monocular camera info for training
├── camera_info_train_multi.json         # Multi-view camera info for training
│
├── {scene_name}.hipnc                   # Houdini source file (simulation or scene setup)
├── particles/                           # Ground-truth trajectories
```

For the COLMAP initialization files, please check this [drive](https://drive.google.com/drive/folders/143eGDwJmsn1j7J24XTGVB7xJgH_Z_TOM?usp=drive_link).
## **👩🏻‍💻 Code Implementation**

Please check each branch for integrated code for recent DyNVS methods.

- **MoSca (CVPR 25):** https://github.com/mjmjeong/PhysGaia/tree/MoSca
- **Shape-of-motion (ICCV 25):** https://github.com/mjmjeong/PhysGaia/tree/SOM
- **4DGS (CVPR 24):** https://github.com/mjmjeong/PhysGaia/tree/4DGS_hex
- **STG (CVPR 24):** https://github.com/mjmjeong/PhysGaia/tree/spacetime
- **D-3DGS (CVPR 24):** https://github.com/mjmjeong/PhysGaia/tree/deformable

## **💳 Citation**

```bash
@inproceedings{kim2026physgaia,
              title={PhysGaia:Physics-Aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis},
              author={Kim, Mijeong and Kim, Gunhee and Choi, Jungyoon and Roh, Wonjae and Han, Bohyung},
              booktitle={CVPR},
              year={2026}
            } 
```

## 🤝 Contributing

We welcome contributions to expand the dataset (additional modality for new downstream tasks, implementation for other models, etc.)

Reach out via opening an issue/discussion in the repo.

## 🛠️ Future Plans

- Update fidelity of the generated scenes
- Add more easier scenes: providing more accessible starting points
- Add guidelines using Houdini source files: ex) How to obtain a flow field?

## **💳 License**

This project is released under the Creative Commons Attribution-NonCommercial 4.0 license.

*✅ Free to use, share, and adapt for non-commercial research*

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