cauphe/MultiCamVideo

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📷 MultiCamVideo Dataset

1. Dataset Introduction

TL;DR: The MultiCamVideo Dataset, introduced in ReCamMaster, is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera trajectories. The MultiCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction.

The MultiCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera trajectories. It consists of 13.6K different dynamic scenes, each captured by 10 cameras, resulting in a total of 136K videos and 112K different camera trajectories. 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 move along predefined trajectories to render the multi-camera video data.

3D Environment: We collect 37 high-quality 3D environments assets from Fab. 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 and Mixamo.

Animation: We collect 93 different animations from Fab and Mixamo, 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 ensure camera movements are diverse and closely resemble real-world distributions, we create a wide range of camera trajectories and parameters to cover various situations. To achieve this by designing rules to batch-generate random camera starting positions and movement trajectories:

1. Camera Starting Position.

We take the character's position as the center of a hemisphere with a radius of {3m, 5m, 7m, 10m} based on the size of the 3D scene and randomly sample within this range as the camera's starting point, ensuring the closest distance to the character is greater than 0.5m and the pitch angle is within 45 degrees.

2. Camera Trajectories.

• Pan & Tilt:
  The camera rotation angles are randomly selected within the range, with pan angles ranging from 5 to 45 degrees and tilt angles ranging from 5 to 30 degrees, with directions randomly chosen left/right or up/down.

• Basic Translation:
  The camera translates along the positive and negative directions of the xyz axes, with movement distances randomly selected within the range of $[\frac{1}{4},1]\times [\backslash frac\{1\}\{4\},\; 1]\; \backslash times$ distance2character.

• Basic Arc Trajectory:
  The camera moves along an arc, with rotation angles randomly selected within the range of 15 to 75 degrees.

• Random Trajectories:
  1-3 points are sampled in space, and the camera moves from the initial position through these points as the movement trajectory, with the total movement distance randomly selected within the range of $[\frac{1}{4},1]\times [\backslash frac\{1\}\{4\},\; 1]\; \backslash times$ distance2character. The polyline is smoothed to make the movement more natural.

• Static Camera:
  The camera does not translate or rotate during shooting, maintaining a fixed position.

3. Camera Movement Speed.

To further enhance the diversity of trajectories, 50% of the training data uses constant-speed camera trajectories, while the other 50% uses variable-speed trajectories generated by nonlinear functions. Consider a camera trajectory with a total of $ff$ frames, starting at location $L_{start}L\_\{start\}$ and ending at position $L_{end}L\_\{end\}$. The location at the $ii$-th frame is given by: $L_i=L_{start}+(L_{end}-L_{start})\cdot \left(\frac{1-\exp (-a\cdot i\mathrm{/}f)}{1-\exp (-a)}\right),L\_i\; =\; L\_\{start\}\; +\; (L\_\{end\}\; -\; L\_\{start\})\; \backslash cdot\; \backslash left(\; \backslash frac\{1\; -\; \backslash exp(-a\; \backslash cdot\; i/f)\}\{1\; -\; \backslash exp(-a)\}\; \backslash right),$

where $aa$ is an adjustable parameter to control the trajectory speed. When $a>0a\; >\; 0$, the trajectory starts fast and then slows down; when , the trajectory starts slow and then speeds up. The larger the absolute value of $aa$, the more drastic the change.

4. Camera Parameters.

We chose four set of camera parameters: {focal=18mm, aperture=10}, {focal=24mm, aperture=5}, {focal=35mm, aperture=2.4} and {focal=50mm, aperture=2.4}.

2. Statistics and Configurations

Dataset Statistics:

Number of Dynamic Scenes	Camera per Scene	Total Videos
13,600	10	136,000

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
18mm, 24mm, 35mm, 50mm	10.0, 5.0, 2.4	23.76mm	23.76mm

3. File Structure

MultiCamVideo-Dataset
├── train
│   ├── f18_aperture10
│   │   ├── 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
│   ├── f24_aperture5
│   │   ├── scene1
│   │   ├── ...
│   │   └── scene3400
│   ├── f35_aperture2.4
│   │   ├── scene1
│   │   ├── ...
│   │   └── scene3400
│   └── f50_aperture2.4
│       ├── scene1
│       ├── ...
│       └── scene3400
└── val
    └── 10basic_trajectories
        ├── videos
        │   ├── cam01.mp4    # example videos corresponding to the validation cameras
        │   ├── cam02.mp4
        │   ├── ...
        │   └── cam10.mp4
        └── cameras
            └── camera_extrinsics.json    # 10 different trajectories for validation

4. Useful scripts

• Data Extraction

sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset
cat MultiCamVideo-Dataset.part* > MultiCamVideo-Dataset.tar.gz
tar --zstd -xvf CamCloneDataset.tar.gz

• Camera Visualization

python vis_cam.py

The visualization script is modified from CameraCtrl, thanks for their inspiring work.

Citation

If you found this dataset useful, please cite our paper.

@misc{bai2025recammaster,
      title={ReCamMaster: Camera-Controlled Generative Rendering from A Single Video}, 
      author={Jianhong Bai and Menghan Xia and Xiao Fu and Xintao Wang and Lianrui Mu and Jinwen Cao and Zuozhu Liu and Haoji Hu and Xiang Bai and Pengfei Wan and Di Zhang},
      year={2025},
      eprint={2503.11647},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.11647}, 
}

Contact

jianghongbai@zju.edu.cn

Acknowledgments

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