# Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On

arXiv: https://arxiv.org/abs/2404.01089 · HTML: https://mlsanity.com/paper/2404.01089/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 40 of its 59 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://mlsanity.com/paper/2202.09778/) (2022)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [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)
- [View Synthesis by Appearance Flow](https://mlsanity.com/paper/1605.03557/) (2016)
- [DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion](https://mlsanity.com/paper/2304.06025/) (2023)
- [LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On](https://mlsanity.com/paper/2305.13501/) (2023)
- [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)
- [TryOnDiffusion: A Tale of Two UNets](https://mlsanity.com/paper/2306.08276/) (2023)
- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)
- [Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow](https://mlsanity.com/paper/2308.06101/) (2023)
- [GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning](https://mlsanity.com/paper/2303.13756/) (2023)

## Built on by (most cited first, 30 of 38)

- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training](https://mlsanity.com/paper/2408.06047/) (2024)
- [Learning Flow Fields in Attention for Controllable Person Image Generation](https://mlsanity.com/paper/2412.08486/) (2024)
- [Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction](https://mlsanity.com/paper/2505.16980/) (2025)
- [ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On](https://mlsanity.com/paper/2503.20418/) (2025)
- [Visual Persona: Foundation Model for Full-Body Human Customization](https://mlsanity.com/paper/2503.15406/) (2025)
- [ShoulderShot: Generating Over-the-Shoulder Dialogue Videos](https://mlsanity.com/paper/2508.07597/) (2025)
- [Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model](https://mlsanity.com/paper/2503.16065/) (2025)
- [VTBench: Comprehensive Benchmark Suite Towards Real-World Virtual Try-on Models](https://mlsanity.com/paper/2505.19571/) (2025)
- [Try Harder: Hard Sample Generation and Learning for Clothes-Changing Person Re-ID](https://mlsanity.com/paper/2507.11119/) (2025)
- [Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling](https://mlsanity.com/paper/2501.04666/) (2025)
- [VTONQA: A Multi-Dimensional Quality Assessment Dataset for Virtual Try-on](https://mlsanity.com/paper/2601.02945/) (2026)
- [DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation](https://mlsanity.com/paper/2607.02220/) (2026)
- [SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models](https://mlsanity.com/paper/2504.13378/) (2025)
- [MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization](https://mlsanity.com/paper/2508.08488/) (2025)
- [DualFit: A Two-Stage Virtual Try-On via Warping and Synthesis](https://mlsanity.com/paper/2508.12131/) (2025)
- [Dress&Dance: Dress up and Dance as You Like It - Technical Preview](https://mlsanity.com/paper/2508.21070/) (2025)
- [FitControler: Toward Fit-Aware Virtual Try-On](https://mlsanity.com/paper/2512.24016/) (2025)
- [OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation](https://mlsanity.com/paper/2601.22725/) (2026)
- [DH-VTON: Deep Text-Driven Virtual Try-On via Hybrid Attention Learning](https://mlsanity.com/paper/2410.12501/) (2024)
- [CORAL: Correspondence Alignment for Improved Virtual Try-On](https://mlsanity.com/paper/2602.17636/) (2026)
- [VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-On](https://mlsanity.com/paper/2603.11734/) (2026)
- [DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport](https://mlsanity.com/paper/2605.12939/) (2026)
- [ModaFlow: Modality-Aware Flow Matching for High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2606.27773/) (2026)
- [Fashionability-Enhancing Outfit Image Editing with Conditional Diffusion Models](https://mlsanity.com/paper/2412.18421/) (2024)
- [Fine-Grained Controllable Apparel Showcase Image Generation via Garment-Centric Outpainting](https://mlsanity.com/paper/2503.01294/) (2025)
- [Diffusion Model-Based Size Variable Virtual Try-On Technology and Evaluation Method](https://mlsanity.com/paper/2504.00562/) (2025)
- [Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis](https://mlsanity.com/paper/2508.04551/) (2025)
- [Undress to Redress: A Training-Free Framework for Virtual Try-On](https://mlsanity.com/paper/2508.07680/) (2025)
- [Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image -- Technical Preview](https://mlsanity.com/paper/2509.04450/) (2025)

## Abstract (arXiv)

Image-based virtual try-on is an increasingly important task for online shopping. It aims to synthesize images of a specific person wearing a specified garment. Diffusion model-based approaches have recently become popular, as they are excellent at image synthesis tasks. However, these approaches usually employ additional image encoders and rely on the cross-attention mechanism for texture transfer from the garment to the person image, which affects the try-on's efficiency and fidelity. To address these issues, we propose an Texture-Preserving Diffusion (TPD) model for virtual try-on, which enhances the fidelity of the results and introduces no additional image encoders. Accordingly, we make contributions from two aspects. First, we propose to concatenate the masked person and reference garment images along the spatial dimension and utilize the resulting image as the input for the diffusion model's denoising UNet. This enables the original self-attention layers contained in the diffusion model to achieve efficient and accurate texture transfer. Second, we propose a novel diffusion-based method that predicts a precise inpainting mask based on the person and reference garment images, further enhancing the reliability of the try-on results. In addition, we integrate mask prediction and image synthesis into a single compact model. The experimental results show that our approach can be applied to various try-on tasks, e.g., garment-to-person and person-to-person try-ons, and significantly outperforms state-of-the-art methods on popular VITON, VITON-HD databases.