📄 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

📄 Citation

If you find this work useful, please consider citing:

@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 for non-commercial use only. Please note that any commercial use of this code requires formal permission prior to use.

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