README.md

---
language: 
- en
license: "apache-2.0"
---
[Github](https://github.com/KwaiVGI/SynCamMaster) 

[Project Page](https://jianhongbai.github.io/SynCamMaster/) 

[Paper](https://arxiv.org/abs/2412.07760)

## 📷 Dataset: SynCamVideo Dataset

- __[2025.04.15]__: Release a new version of the SynCamVideo Dataset with improved quality and greater diversity.
- __[2025.04.15]__: Please also check our [MultiCamVideo](https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset) Dataset.
### 1. Dataset Introduction

**TL;DR:** The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses. The SynCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction. The camera is stationary in the SynCamVideo Dataset. If you require footage with moving cameras rather than stationary ones, please explore our [MultiCamVideo](https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset) Dataset.

<div align="center">
  <video controls autoplay style="width: 70%;" src="https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/qEUQstpMa3-6UjbG_0ytq.mp4"></video>
</div>

The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses.
It consists of 3.4K different dynamic scenes, each captured by 10 cameras, resulting in a total of 34K videos. Each dynamic scene is composed of four elements: {3D environment, character, animation, camera}. Specifically, we use animation to drive the character 
and position the animated character within the 3D environment. Then, Time-synchronized cameras are set up to render the multi-camera video data.
<p align="center">
  <img src="https://github.com/user-attachments/assets/107c9607-e99b-4493-b715-3e194fcb3933" alt="Example Image" width="70%">
</p>

**3D Environment:** We collect 37 high-quality 3D environments assets from [Fab](https://www.fab.com). To minimize the domain gap between rendered data and real-world videos, we primarily select visually realistic 3D scenes, while choosing a few stylized or surreal 3D scenes as a supplement. To ensure data diversity, the selected scenes cover a variety of indoor and outdoor settings, such as city streets, shopping malls, cafes, office rooms, and the countryside.

**Character:** We collect 66 different human 3D models as characters from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com).

**Animation:** We collect 93 different animations from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com), including common actions such as waving, dancing, and cheering. We use these animations to drive the collected characters and create diverse datasets through various combinations.

**Camera:** To enhance the diversity of the dataset, each camera is randomly sampled on a hemispherical surface centered around the character.

### 2. Statistics and Configurations

Dataset Statistics:

| Number of Dynamic Scenes | Camera per Scene | Total Videos |
|:------------------------:|:----------------:|:------------:|
| 3400                   | 10               | 34,000      |

Video Configurations:

| Resolution  | Frame Number | FPS                      |
|:-----------:|:------------:|:------------------------:|
| 1280x1280   | 81           | 15                       |

Note: You can use 'center crop' to adjust the video's aspect ratio to fit your video generation model, such as 16:9, 9:16, 4:3, or 3:4.

Camera Configurations:

| Focal Length            | Aperture           | Sensor Height | Sensor Width |
|:-----------------------:|:------------------:|:-------------:|:------------:|
| 24mm  | 5.0     | 23.76mm       | 23.76mm      |



### 3. File Structure
```
SynCamVideo-Dataset
├── train
│   └── f24_aperture5
│       ├── scene1    # one dynamic scene
│       │   ├── videos
│       │   │   ├── cam01.mp4    # synchronized 81-frame videos at 1280x1280 resolution
│       │   │   ├── cam02.mp4
│       │   │   ├── ...
│       │   │   └── cam10.mp4
│       │   └── cameras
│       │       └── camera_extrinsics.json    # 81-frame camera extrinsics of the 10 cameras 
│       ├── ...
│       └── scene3400
└── val
    └── basic
        ├── videos
        │   ├── cam01.mp4    # example videos corresponding to the validation cameras
        │   ├── cam02.mp4
        │   ├── ...
        │   └── cam10.mp4
        └── cameras
            └── camera_extrinsics.json    # 10 cameras for validation
```

### 3. Useful scripts
- Data Extraction
```bash
tar -xzvf SynCamVideo-Dataset.tar.gz
```
- Camera Visualization
```python
python vis_cam.py
```

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/3WCWS0Axlnu5MyOBqMoVC.png" alt="Example Image" width="40%">
</p>

## Acknowledgments
We thank Jinwen Cao, Yisong Guo, Haowen Ji, Jichao Wang, and Yi Wang from Kuaishou Technology for their invaluable help in constructing the SynCamVideo-Dataset.

## 🌟 Citation

Please cite our paper if you find our dataset helpful.
```
@misc{bai2024syncammaster,
      title={SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints}, 
      author={Jianhong Bai and Menghan Xia and Xintao Wang and Ziyang Yuan and Xiao Fu and Zuozhu Liu and Haoji Hu and Pengfei Wan and Di Zhang},
      year={2024},
      eprint={2412.07760},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.07760}, 
}
```

## Contact

[Jianhong Bai](https://jianhongbai.github.io/)