---
language:
- en
license: mit
multimodality:
- text
- image
- video
tags:
- text-to-video
- personalization
- motion-customization
- subject-customization
task_categories:
- text-to-image
- text-to-video
size_categories:
- n<1K
---

# Subject Motion Dataset

A dataset for personalized text-to-video generation, supporting subject customization, motion customization, and subject-motion combination customization.

## Dataset Description

Subject Motion Dataset is a images and videos dataset specifically designed for personalized text-to-video generation tasks. The dataset consists of two main components:

- **Subject**: 16 different subjects, each containing 4-6 high-quality images
- **Motion**: 10 different motion videos covering various dynamic behaviors

## Dataset Structure

```
subject_motion/
├── subject/
│   ├── Terracotta_Warriors/                   
│   ├── red_cartoon/           
│   ├── cat3D/                 
│   ├── wolf_plushie/         
│   ├── grey_sloth_plushie/    
│   ├── cat2/                  
│   ├── stitch/                
│   ├── dog2/                 
│   ├── porcupine/             
│   ├── monster_toy/           
│   ├── dog/                   
│   ├── robot_toy/             
│   ├── pig/                   
│   ├── bear_plushie/          
│   ├── dog6/                  
│   └── cat/                   
└── motion/                    
    ├── Cycling/               
    ├── diving/                
    ├── ski/                   
    ├── dog_skateboard/        
    ├── surf/                  
    ├── man_skateboard/        
    ├── ride/                  
    ├── rotating/              
    ├── play_guitar/           
    └── horse_running/         
```

## Data Sources

### Subject Data
Subject images are sourced from three channels:
- **DreamBooth**: Based on the paper [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://arxiv.org/abs/2208.12242)
- **The Chosen One**: Based on the paper [The Chosen One: Consistent Characters in Text-to-Image Diffusion Models](https://arxiv.org/abs/2311.10093)
- **Web Collection**: High-quality subject images collected from the web

### Motion Data
All motion videos are collected from the web, carefully curated to ensure quality and diversity.

## Applications

This dataset is primarily used for three types of customization generation:

1. **Subject Customization**: Using specific subject images for personalized subject generation
2. **Motion Customization**: Learning motion styles based on specific motion videos
3. **Subject-Motion Combination Customization**: Combining specific subjects with specific motions to generate personalized subject-motion combinations


## Technical Features

- **High Quality**: All images and videos are quality-filtered
- **Diversity**: Covers various subject types and motion types
- **Standardization**: Unified data format and naming conventions
- **Extensibility**: Supports adding new subjects and motions

## Citation

If you use this dataset in your research, please cite this dataset and the related papers:

```bibtex
@misc{sun2025,
  author = {Chenhao Sun},
  title = {Subject Motion Dataset},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Minusone/subject_motion}},
  note = {Accessed: 2025-07-20}
}

@inproceedings{ruiz2023dreambooth,
  title={Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation},
  author={Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and Pritch, Yael and Rubinstein, Michael and Aberman, Kfir},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={22500--22510},
  year={2023}
}

@article{Avrahami_Hertz_Vinker_Arar_Fruchter_Fried_Cohen-Or_Lischinski,  
title={The Chosen One: Consistent Characters in Text-to-Image Diffusion Models}, 
author={Avrahami, Omri and Hertz, Amir and Vinker, Yael and Arar, Moab and Fruchter, Shlomi and Fried, Ohad and Cohen-Or, Daniel and Lischinski, Dani}, 
language={en-US} 
}
```

## License

This dataset is licensed under the MIT License.

## Contributing

We welcome issues and pull requests to improve this dataset.

## Contact

For questions or suggestions, please contact us through GitHub Issues.