# FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion

arXiv: https://arxiv.org/abs/2206.11768 · HTML: https://mlsanity.com/paper/2206.11768/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [Semi-Supervised StyleGAN for Disentanglement Learning](https://mlsanity.com/paper/2003.03461/) (2020)
- [TryOnGAN: Body-Aware Try-On via Layered Interpolation](https://mlsanity.com/paper/2101.02285/) (2021)
- [Generating High-Resolution Fashion Model Images Wearing Custom Outfits](https://mlsanity.com/paper/1908.08847/) (2019)
- [ViBE: Dressing for Diverse Body Shapes](https://mlsanity.com/paper/1912.06697/) (2019)
- [Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping](https://mlsanity.com/paper/1609.02489/) (2016)
- [A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce](https://mlsanity.com/paper/1907.09844/) (2019)
- [A Hierarchical Bayesian Model for Size Recommendation in Fashion](https://mlsanity.com/paper/1908.00825/) (2019)
- [SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images](https://mlsanity.com/paper/1905.11784/) (2019)
- [Analyzing Customer Feedback for Product Fit Prediction](https://mlsanity.com/paper/1908.10896/) (2019)

## Built on by (most cited first, 4 of 4)

- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [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)
- [SizeGAN: Improving Size Representation in Clothing Catalogs](https://mlsanity.com/paper/2211.02892/) (2022)

## Abstract (arXiv)

Amidst the rapid growth of fashion e-commerce, remote fitting of fashion articles remains a complex and challenging problem and a main driver of customers' frustration. Despite the recent advances in 3D virtual try-on solutions, such approaches still remain limited to a very narrow - if not only a handful - selection of articles, and often for only one size of those fashion items. Other state-of-the-art approaches that aim to support customers find what fits them online mostly require a high level of customer engagement and privacy-sensitive data (such as height, weight, age, gender, belly shape, etc.), or alternatively need images of customers' bodies in tight clothing. They also often lack the ability to produce fit and shape aware visual guidance at scale, coming up short by simply advising which size to order that would best match a customer's physical body attributes, without providing any information on how the garment may fit and look. Contributing towards taking a leap forward and surpassing the limitations of current approaches, we present FitGAN, a generative adversarial model that explicitly accounts for garments' entangled size and fit characteristics of online fashion at scale. Conditioned on the fit and shape of the articles, our model learns disentangled item representations and generates realistic images reflecting the true fit and shape properties of fashion articles. Through experiments on real world data at scale, we demonstrate how our approach is capable of synthesizing visually realistic and diverse fits of fashion items and explore its ability to control fit and shape of images for thousands of online garments.