# SizeGAN: Improving Size Representation in Clothing Catalogs

arXiv: https://arxiv.org/abs/2211.02892 · HTML: https://mlsanity.com/paper/2211.02892/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [Style-Based Global Appearance Flow for Virtual Try-On](https://mlsanity.com/paper/2204.01046/) (2022)
- [Graphonomy: Universal Human Parsing via Graph Transfer Learning](https://mlsanity.com/paper/1904.04536/) (2019)
- [Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing](https://mlsanity.com/paper/2104.07021/) (2021)
- [Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN](https://mlsanity.com/paper/2109.06166/) (2021)
- [TryOnGAN: Body-Aware Try-On via Layered Interpolation](https://mlsanity.com/paper/2101.02285/) (2021)
- [Learning optical flow from still images](https://mlsanity.com/paper/2104.03965/) (2021)
- [ClothFormer:Taming Video Virtual Try-on in All Module](https://mlsanity.com/paper/2204.12151/) (2022)
- [Weakly Supervised High-Fidelity Clothing Model Generation](https://mlsanity.com/paper/2112.07200/) (2021)
- [Structure-Aware Flow Generation for Human Body Reshaping](https://mlsanity.com/paper/2203.04670/) (2022)
- [FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion](https://mlsanity.com/paper/2206.11768/) (2022)

## Built on by (most cited first, 1 of 1)

- [Tailor: Size Recommendations for High-End Fashion Marketplaces](https://mlsanity.com/paper/2401.01978/) (2024)

## Abstract (arXiv)

Online clothing catalogs lack diversity in body shape and garment size. Brands commonly display their garments on models of one or two sizes, rarely including plus-size models. To our knowledge, our paper presents the first method for generating images of garments and models in a new target size to tackle the size under-representation problem. Our primary technical contribution is a conditional generative adversarial network that learns deformation fields at multiple resolutions to realistically change the size of models and garments. Results from our two user studies show SizeGAN outperforms alternative methods along three dimensions -- realism, garment faithfulness, and size -- which are all important for real world use.