# GarmentGAN: Photo-realistic Adversarial Fashion Transfer

arXiv: https://arxiv.org/abs/2003.01894 · HTML: https://mlsanity.com/paper/2003.01894/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Toward Accurate and Realistic Outfits Visualization with Attention to Details](https://mlsanity.com/paper/2106.06593/) (2021)
- [TryOnGAN: Body-Aware Try-On via Layered Interpolation](https://mlsanity.com/paper/2101.02285/) (2021)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems](https://mlsanity.com/paper/2111.15140/) (2021)
- [Shape Controllable Virtual Try-on for Underwear Models](https://mlsanity.com/paper/2107.13156/) (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)
- [Optimizing Latent Space Directions For GAN-based Local Image Editing](https://mlsanity.com/paper/2111.12583/) (2021)
- [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)
- [Mobile-VTON: High-Fidelity On-Device Virtual Try-On](https://mlsanity.com/paper/2603.00947/) (2026)
- [Significance of Skeleton-based Features in Virtual Try-On](https://mlsanity.com/paper/2208.08076/) (2022)
- [Controllable Garment Transfer](https://mlsanity.com/paper/2204.01965/) (2022)
- [Significance of Anatomical Constraints in Virtual Try-On](https://mlsanity.com/paper/2401.02110/) (2024)
- [AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles](https://mlsanity.com/paper/2608.06801/) (2026)

## Abstract (arXiv)

The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN requires as input only two images, namely, a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. In order to make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body key-point information. We show qualitative and quantitative comparisons to several other networks to demonstrate the effectiveness of this technique.