README.md

May 15, 2026 · View on GitHub

[SIGGRAPH Asia'25] Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off

arxiv Project Page License

Seungyong Lee*   Jeong-gi Kwak*
* Equal contribution
NXN LABS

Voost Teaser
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] 🎉 Voost has been accepted to SIGGRAPH Asia 2025!
  • [2025-08-19] 🚀 A public demo of Voost is now available on Hugging Face Spaces. Try it out and don’t forget to leave a 🤍 like to support us!
  • [2025-08-08] 📄 Voost is now on arXiv. Visit the project page for more details and results.

Demo Hugging Face Demo

🚀 A public demo (try-on & try-off) is now available on Hugging Face Spaces.
Try it out and don’t forget to leave a 🤍 like to support us!

demo-ui

Examples

demo 1 demo 2 demo 3 demo 4 demo 5

Citation

@inproceedings{lee2025voost,
  title={Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off},
  author={Lee, Seungyong and Kwak, Jeong-gi},
  booktitle={Proceedings of the SIGGRAPH Asia 2025 Conference Papers},
  pages={1--11},
  year={2025}
}

License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

For commercial use, please visit NXN Labs.

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.