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--- |
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license: apache-2.0 |
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task_categories: |
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- image-to-video |
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tags: |
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- video-generation |
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- trajectory-control |
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- segmentation |
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- bounding-box |
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--- |
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[[Paper](https://arxiv.org/pdf/2503.16421)] [[Project Page](https://quanhaol.github.io/magicmotion-site/)] [[Github](https://github.com/quanhaol/MagicMotion)] |
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## 🧙 MagicData Dataset |
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### 1. Dataset Introduction |
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The MagicData Dataset, introduced in [MagicMotion](https://arxiv.org/pdf/2503.16421), is a comprehensive trajectory controllable video generation dataset with rich semantic annotations. This dataset features high-quality videos with precise segmentation masks and bounding boxes annotations, designed to advance the field of controllable video synthesis and understanding. |
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The dataset consists of 23K diverse videos, each meticulously annotated with both pixel-level segmentation masks and object-level bounding boxes. Each video in the dataset is carefully curated to ensure high visual quality and annotation accuracy, making it suitable for training state-of-the-art video generation and understanding models. |
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### 2. File Structure |
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``` |
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MagicData |
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├── videos |
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│ ├── videoid_1.mp4 |
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│ ├── videoid_2.mp4 |
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│ ├── ... |
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├── masks |
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│ ├── videoid_1 |
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│ │ ├── annotated_frame_00000.png |
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│ │ ├── annotated_frame_00001.png |
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│ │ ├── ... |
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│ ├── videoid_2 |
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│ │ ├── ... |
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├── boxs |
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│ ├── videoid_1 |
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│ │ ├── annotated_frame_00000.png |
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│ │ ├── annotated_frame_00001.png |
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│ │ ├── ... |
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│ ├── videoid_2 |
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│ │ ├── ... |
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├── MagicData.csv # detailed information of each video |
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``` |
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### 3. Useful scripts |
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- Data Extraction |
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```bash |
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sudo apt-get install git-lfs |
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git lfs install |
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git clone https://huggingface.co/datasets/quanhaol/MagicData |
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tar -xzvf boxs.tar.gz |
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cat masks.tar.gz.part* > masks.tar.gz |
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tar -xzvf masks.tar.gz |
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cat videos.zip.part_* > videos.zip |
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unzip videos.zip |
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``` |
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## Citation |
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If you found this dataset useful, please cite our [paper](https://arxiv.org/pdf/2503.16421). |
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```bibtex |
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@article{li2025magicmotion, |
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title={Magicmotion: Controllable video generation with dense-to-sparse trajectory guidance}, |
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author={Li, Quanhao and Xing, Zhen and Wang, Rui and Zhang, Hui and Dai, Qi and Wu, Zuxuan}, |
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journal={arXiv preprint arXiv:2503.16421}, |
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year={2025} |
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} |
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``` |
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## Contact |
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[liqh24@m.fudan.edu.cn](liqh24@m.fudan.edu.cn) |
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[zxing20@fudan.edu.cn](zxing20@fudan.edu.cn) |
