# VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss

arXiv: https://arxiv.org/abs/1911.07926 · HTML: https://mlsanity.com/paper/1911.07926/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment](https://mlsanity.com/paper/2403.12965/) (2024)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems](https://mlsanity.com/paper/2111.15140/) (2021)
- [TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On](https://mlsanity.com/paper/2411.17017/) (2024)
- [MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input](https://mlsanity.com/paper/2503.08650/) (2025)
- [Design2Cloth: 3D Cloth Generation from 2D Masks](https://mlsanity.com/paper/2404.02686/) (2024)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation](https://mlsanity.com/paper/2601.22725/) (2026)
- [Mobile-VTON: High-Fidelity On-Device Virtual Try-On](https://mlsanity.com/paper/2603.00947/) (2026)
- [ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On](https://mlsanity.com/paper/2506.05858/) (2025)
- [Interpreting CNN Predictions using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2301.08067/) (2023)
- [From Keypoints to Realism: A Realistic and Accurate Virtual Try-on Network from 2D Images](https://mlsanity.com/paper/2504.03807/) (2025)
- [PROMO: Promptable Outfitting for Efficient High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2603.11675/) (2026)
- [Controllable Clothing: Precise Labels and Generation for Virtual Try-On with Latent Diffusion Models](https://mlsanity.com/paper/2608.05834/) (2026)

## Abstract (arXiv)

Generating a virtual try-on image from in-shop clothing images and a model person's snapshot is a challenging task because the human body and clothes have high flexibility in their shapes. In this paper, we develop a Virtual Try-on Generative Adversarial Network (VITON-GAN), that generates virtual try-on images using images of in-shop clothing and a model person. This method enhances the quality of the generated image when occlusion is present in a model person's image (e.g., arms crossed in front of the clothes) by adding an adversarial mechanism in the training pipeline.