# Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN

arXiv: https://arxiv.org/abs/2111.10544 · HTML: https://mlsanity.com/paper/2111.10544/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [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)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis](https://mlsanity.com/paper/1909.12224/) (2019)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)
- [Towards Multi-pose Guided Virtual Try-on Network](https://mlsanity.com/paper/1902.11026/) (2019)
- [Graphonomy: Universal Human Parsing via Graph Transfer Learning](https://mlsanity.com/paper/1904.04536/) (2019)
- [Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark](https://mlsanity.com/paper/1804.01984/) (2018)
- [Disentangled Cycle Consistency for Highly-realistic Virtual Try-On](https://mlsanity.com/paper/2103.09479/) (2021)
- [Neural Re-Rendering of Humans from a Single Image](https://mlsanity.com/paper/2101.04104/) (2021)
- [Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing](https://mlsanity.com/paper/2104.07021/) (2021)
- [Style and Pose Control for Image Synthesis of Humans from a Single Monocular View](https://mlsanity.com/paper/2102.11263/) (2021)
- [On Aliased Resizing and Surprising Subtleties in GAN Evaluation](https://mlsanity.com/paper/2104.11222/) (2021)
- [HumanGAN: A Generative Model of Humans Images](https://mlsanity.com/paper/2103.06902/) (2021)
- [M3D-VTON: A Monocular-to-3D Virtual Try-On Network](https://mlsanity.com/paper/2108.05126/) (2021)
- [WAS-VTON: Warping Architecture Search for Virtual Try-on Network](https://mlsanity.com/paper/2108.00386/) (2021)
- [STN-Homography: estimate homography parameters directly](https://mlsanity.com/paper/1906.02539/) (2019)

## Built on by (most cited first, 30 of 40)

- [GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning](https://mlsanity.com/paper/2303.13756/) (2023)
- [CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2407.15886/) (2024)
- [Learning Locally Editable Virtual Humans](https://mlsanity.com/paper/2305.00121/) (2023)
- [Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images](https://mlsanity.com/paper/2311.16094/) (2023)
- [MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation](https://mlsanity.com/paper/2405.00448/) (2024)
- [Dressing in the Wild by Watching Dance Videos](https://mlsanity.com/paper/2203.15320/) (2022)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models](https://mlsanity.com/paper/2411.18350/) (2024)
- [WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on](https://mlsanity.com/paper/2312.03667/) (2023)
- [PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on](https://mlsanity.com/paper/2207.13475/) (2022)
- [D4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On](https://mlsanity.com/paper/2407.15111/) (2024)
- [Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning](https://mlsanity.com/paper/2211.14052/) (2022)
- [UniHuman: A Unified Model for Editing Human Images in the Wild](https://mlsanity.com/paper/2312.14985/) (2023)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [VITON-DiT: Learning In-the-Wild Video Try-On from Human Dance Videos via Diffusion Transformers](https://mlsanity.com/paper/2405.18326/) (2024)
- [A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidance](https://mlsanity.com/paper/2312.15480/) (2023)
- [FashionTex: Controllable Virtual Try-on with Text and Texture](https://mlsanity.com/paper/2305.04451/) (2023)
- [OmniVTON: Training-Free Universal Virtual Try-On](https://mlsanity.com/paper/2507.15037/) (2025)
- [Vision + Language Applications: A Survey](https://mlsanity.com/paper/2305.14598/) (2023)
- [DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models](https://mlsanity.com/paper/2407.16511/) (2024)
- [Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off](https://mlsanity.com/paper/2508.04825/) (2025)
- [PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm](https://mlsanity.com/paper/2304.08956/) (2023)
- [Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism](https://mlsanity.com/paper/2412.09822/) (2024)
- [MGT: Extending Virtual Try-Off to Multi-Garment Scenarios](https://mlsanity.com/paper/2504.13078/) (2025)
- [iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance](https://mlsanity.com/paper/2605.21431/) (2026)
- [Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling](https://mlsanity.com/paper/2501.04666/) (2025)
- [Progressive Limb-Aware Virtual Try-On](https://mlsanity.com/paper/2503.12588/) (2025)
- [OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation](https://mlsanity.com/paper/2601.22725/) (2026)
- [Significance of Skeleton-based Features in Virtual Try-On](https://mlsanity.com/paper/2208.08076/) (2022)
- [Single Stage Multi-Pose Virtual Try-On](https://mlsanity.com/paper/2211.10715/) (2022)

## Abstract (arXiv)

Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. Yet, as most try-on approaches fit in-shop garments onto a target person, they require the laborious and restrictive construction of a paired training dataset, severely limiting their scalability. While a few recent works attempt to transfer garments directly from one person to another, alleviating the need to collect paired datasets, their performance is impacted by the lack of paired (supervised) information. In particular, disentangling style and spatial information of the garment becomes a challenge, which existing methods either address by requiring auxiliary data or extensive online optimization procedures, thereby still inhibiting their scalability. To achieve a scalable virtual try-on system that can transfer arbitrary garments between a source and a target person in an unsupervised manner, we thus propose a texture-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive GAN (PASTA-GAN), that facilitates real-world unpaired virtual try-on. Specifically, to disentangle the style and spatial information of each garment, PASTA-GAN consists of an innovative patch-routed disentanglement module for successfully retaining garment texture and shape characteristics. Guided by the source person keypoints, the patch-routed disentanglement module first decouples garments into normalized patches, thus eliminating the inherent spatial information of the garment, and then reconstructs the normalized patches to the warped garment complying with the target person pose. Given the warped garment, PASTA-GAN further introduces novel spatially-adaptive residual blocks that guide the generator to synthesize more realistic garment details.