# Toward Accurate and Realistic Outfits Visualization with Attention to Details

arXiv: https://arxiv.org/abs/2106.06593 · HTML: https://mlsanity.com/paper/2106.06593/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 65 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 30 of 45)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [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)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [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)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [Effectively Unbiased FID and Inception Score and where to find them](https://mlsanity.com/paper/1911.07023/) (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)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)
- [Convolutional neural network architecture for geometric matching](https://mlsanity.com/paper/1703.05593/) (2017)
- [Towards Multi-pose Guided Virtual Try-on Network](https://mlsanity.com/paper/1902.11026/) (2019)

## Built on by (most cited first, 30 of 33)

- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On](https://mlsanity.com/paper/2305.13501/) (2023)
- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)
- [GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning](https://mlsanity.com/paper/2303.13756/) (2023)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Single Stage Virtual Try-on via Deformable Attention Flows](https://mlsanity.com/paper/2207.09161/) (2022)
- [CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model](https://mlsanity.com/paper/2311.18405/) (2023)
- [Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2404.01089/) (2024)
- [StableGarment: Garment-Centric Generation via Stable Diffusion](https://mlsanity.com/paper/2403.10783/) (2024)
- [MV-VTON: Multi-View Virtual Try-On with Diffusion Models](https://mlsanity.com/paper/2404.17364/) (2024)
- [GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Prompting](https://mlsanity.com/paper/2405.07472/) (2024)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on](https://mlsanity.com/paper/2312.03667/) (2023)
- [Improving Virtual Try-On with Garment-focused Diffusion Models](https://mlsanity.com/paper/2409.08258/) (2024)
- [HS-Diffusion: Semantic-Mixing Diffusion for Head Swapping](https://mlsanity.com/paper/2212.06458/) (2022)
- [FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on](https://mlsanity.com/paper/2404.14162/) (2024)
- [Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On](https://mlsanity.com/paper/2505.16977/) (2025)
- [AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models](https://mlsanity.com/paper/2412.04146/) (2024)
- [VTBench: Comprehensive Benchmark Suite Towards Real-World Virtual Try-on Models](https://mlsanity.com/paper/2505.19571/) (2025)
- [Learning Garment DensePose for Robust Warping in Virtual Try-On](https://mlsanity.com/paper/2303.17688/) (2023)
- [Limb-Aware Virtual Try-On Network with Progressive Clothing Warping](https://mlsanity.com/paper/2503.14074/) (2025)
- [Modern Augmented Reality: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2202.09450/) (2022)
- [Progressive Limb-Aware Virtual Try-On](https://mlsanity.com/paper/2503.12588/) (2025)
- [WG-VITON: Wearing-Guide Virtual Try-On for Top and Bottom Clothes](https://mlsanity.com/paper/2205.04759/) (2022)
- [ACDG-VTON: Accurate and Contained Diffusion Generation for Virtual Try-On](https://mlsanity.com/paper/2403.13951/) (2024)
- [SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models](https://mlsanity.com/paper/2504.13378/) (2025)
- [GC-VTON: Predicting Globally Consistent and Occlusion Aware Local Flows with Neighborhood Integrity Preservation for Virtual Try-on](https://mlsanity.com/paper/2311.04932/) (2023)
- [SiCo: An Interactive Size-Controllable Virtual Try-On Approach for Informed Decision-Making](https://mlsanity.com/paper/2408.02803/) (2024)
- [Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On](https://mlsanity.com/paper/2412.11435/) (2024)
- [OmniDiT: Extending Diffusion Transformer to Omni-VTON Framework](https://mlsanity.com/paper/2603.19643/) (2026)

## Abstract (arXiv)

Virtual try-on methods aim to generate images of fashion models wearing arbitrary combinations of garments. This is a challenging task because the generated image must appear realistic and accurately display the interaction between garments. Prior works produce images that are filled with artifacts and fail to capture important visual details necessary for commercial applications. We propose Outfit Visualization Net (OVNet) to capture these important details (e.g. buttons, shading, textures, realistic hemlines, and interactions between garments) and produce high quality multiple-garment virtual try-on images. OVNet consists of 1) a semantic layout generator and 2) an image generation pipeline using multiple coordinated warps. We train the warper to output multiple warps using a cascade loss, which refines each successive warp to focus on poorly generated regions of a previous warp and yields consistent improvements in detail. In addition, we introduce a method for matching outfits with the most suitable model and produce significant improvements for both our and other previous try-on methods. Through quantitative and qualitative analysis, we demonstrate our method generates substantially higher-quality studio images compared to prior works for multi-garment outfits. An interactive interface powered by this method has been deployed on fashion e-commerce websites and received overwhelmingly positive feedback.