# Voost
**Repository Path**: monkeycc/Voost
## Basic Information
- **Project Name**: Voost
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-03
- **Last Updated**: 2025-12-03
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[SIGGRAPH Asia'25] Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off

[Seungyong Lee
*](https://ryan-seungyong-lee.github.io/)
[Jeong-gi Kwak
*](https://jgkwak95.github.io/)
* Equal contribution
**[NXN LABS](https://nxn.ai/)**

Voost jointly handles virtual try-on and try-off within a single transformer, achieving high-quality results while
remaining robust to various human poses, garment categories, backgrounds, lighting conditions, and image compositions.
## News
- [2025-10-13] π Voost has been accepted to SIGGRAPH Asia 2025!
- [2025-08-19] π A public demo of Voost is now available on [Hugging Face Spaces](https://huggingface.co/spaces/NXN-Labs/Voost). Try it out and donβt forget to leave a π€ _like_ to support us!
- [2025-08-08] π Voost is now on [arXiv](https://arxiv.org/abs/2508.04825). Visit the [project page](https://nxnai.github.io/Voost/) for more details and results.
## Demo
π A **public demo (try-on & try-off)** is now available on [Hugging Face Spaces](https://huggingface.co/spaces/NXN-Labs/Voost).
Try it out and donβt forget to leave a π€ like to support us!
### Examples
## Citation
```bibtex
@article{lee2025voost,
author = {Seungyong Lee and Jeong-gi Kwak},
title = {Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off},
journal = {arXiv preprint arXiv:2508.04825},
year = {2025}
}
```
## License
This project is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
For commercial use, please visit [NXN Labs](https://nxn.ai/).
## Acknowledgments
Most models and clothing images used are from internet and public datasets (VITON, DressCode). All images and brands are the property of their respective owners.