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[NeurIPS 2025] Official PyTorch implementation of "Mitigating Occlusions in Virtual Try-On via A Simple-Yet-Effective Mask-Free Framework."

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<div align="center">

<h1>Mitigating Occlusions in Virtual Try-On via A Simple-Yet-Effective Mask-Free Framework</h1>

<p>
  <a href="https://github.com/du-chenghu">Chenghu Du</a><sup>1</sup>&nbsp;·&nbsp;
  <a href="https://github.com/">Shengwu Xiong</a><sup>2,3</sup>&nbsp;·&nbsp;
  <a href="https://github.com/">Junyin Wang</a><sup>1</sup>&nbsp;·&nbsp;
  <a href="https://github.com//">Yi Rong</a><sup>1✉</sup>&nbsp;·&nbsp;
  <a href="https://github.com//">Shili Xiong</a><sup>1✉</sup>
</p>

<sup>1</sup> School of Computer Science and Artificial Intelligence, Wuhan University of Technology<br>
<sup>2</sup> Interdisciplinary Artificial Intelligence Research Institute, Wuhan College<br>
<sup>3</sup> Shanghai Artificial Intelligence Laboratory<br>
<sup>✉</sup> Corresponding authors

</div>

## 📄 Abstract

This work tackles occlusion issues in Virtual Try-On (VTON).  
We taxonomize failures into:

1. **Inherent Occlusions** – “ghost” garments from the reference image that remain in the result.  
2. **Acquired Occlusions** – distorted human anatomy that visually blocks the new outfit.

To remove both, we propose a **mask-free VTON framework** with two plug-and-play operations:

- **Background Pre-Replacement** – swaps the background before generation so the model never confuses clothes with body/background, suppressing inherent occlusions.  
- **Covering-and-Eliminating** – enforces human-aware semantics, yielding anatomically plausible shapes and thus fewer acquired occlusions.

The operations are architecture-agnostic: drop them into GANs or diffusion models without re-design.

## 📊 Visualization Results
<div align="center">
  <img src="static/images/appDress_01.png" alt="video" width="100%">
</div>
<div align="center">
  <img src="static/images/appUpper_01.png" alt="video" width="100%">
</div>
<div align="center">
  <img src="static/images/appLower_01.png" alt="video" width="100%">
</div>

## 📄 Citation

If you find this work useful, please consider citing:

```bibtex
@article{du2025mitigating,
  title={Mitigating Occlusions in Virtual Try-On via A Simple-Yet-Effective Mask-Free Framework},
  author={Du, Chenghu and Xiong, Shengwu and Wang, Junyin and Rong, Yi and Xiong, Shili},
  journal={Advances in Neural Information Processing Systems},
  year={2025}
}
```

## 📜 License

This code is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/) for non-commercial use only.
Please note that any commercial use of this code requires formal permission prior to use.

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