# BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training

arXiv: https://arxiv.org/abs/2408.06047 · HTML: https://mlsanity.com/paper/2408.06047/

## Key facts

- Cited by 37 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 43 of its 50 references that are in the map.
- First posted on arXiv 12 Aug 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 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [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)
- [StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On](https://mlsanity.com/paper/2312.01725/) (2023)
- [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)
- [OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://mlsanity.com/paper/2403.01779/) (2024)
- [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)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)

## Built on by (most cited first, 30 of 37)

- [FLUX-Text: A Simple and Advanced Diffusion Transformer Baseline for Scene Text Editing](https://mlsanity.com/paper/2505.03329/) (2025)
- [SCALAR: Scale-wise Controllable Visual Autoregressive Learning](https://mlsanity.com/paper/2507.19946/) (2025)
- [DreamVVT: Mastering Realistic Video Virtual Try-On in the Wild via a Stage-Wise Diffusion Transformer Framework](https://mlsanity.com/paper/2508.02807/) (2025)
- [OmniTry: Virtual Try-On Anything without Masks](https://mlsanity.com/paper/2508.13632/) (2025)
- [FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models](https://mlsanity.com/paper/2508.20586/) (2025)
- [RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation](https://mlsanity.com/paper/2411.19528/) (2024)
- [Group Relative Attention Guidance for Image Editing](https://mlsanity.com/paper/2510.24657/) (2025)
- [JCo-MVTON: Jointly Controllable Multi-Modal Diffusion Transformer for Mask-Free Virtual Try-on](https://mlsanity.com/paper/2508.17614/) (2025)
- [Semantic Context Matters: Improving Conditioning for Autoregressive Models](https://mlsanity.com/paper/2511.14063/) (2025)
- [MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input](https://mlsanity.com/paper/2503.08650/) (2025)
- [Loom: Diffusion-Transformer for Interleaved Generation](https://mlsanity.com/paper/2512.18254/) (2025)
- [CrossVTON: Mimicking the Logic Reasoning on Cross-category Virtual Try-on guided by Tri-zone Priors](https://mlsanity.com/paper/2502.14373/) (2025)
- [UniFit: Towards Universal Virtual Try-on with MLLM-Guided Semantic Alignment](https://mlsanity.com/paper/2511.15831/) (2025)
- [TripVVT: A Large-Scale Triplet Dataset and a Coarse-Mask Baseline for In-the-Wild Video Virtual Try-On](https://mlsanity.com/paper/2604.27958/) (2026)
- [MFP-VTON: Enhancing Mask-Free Person-to-Person Virtual Try-On via Diffusion Transformer](https://mlsanity.com/paper/2502.01626/) (2025)
- [DualFit: A Two-Stage Virtual Try-On via Warping and Synthesis](https://mlsanity.com/paper/2508.12131/) (2025)
- [Visual Generation Tuning](https://mlsanity.com/paper/2511.23469/) (2025)
- [FitControler: Toward Fit-Aware Virtual Try-On](https://mlsanity.com/paper/2512.24016/) (2025)
- [Mobile-VTON: High-Fidelity On-Device Virtual Try-On](https://mlsanity.com/paper/2603.00947/) (2026)
- [AnyDesign: Versatile Area Fashion Editing via Mask-Free Diffusion](https://mlsanity.com/paper/2408.11553/) (2024)
- [DiT-VTON: Diffusion Transformer Framework for Unified Multi-Category Virtual Try-On and Virtual Try-All with Integrated Image Editing](https://mlsanity.com/paper/2510.04797/) (2025)
- [A Style is Worth One Code: Unlocking Code-to-Style Image Generation with Discrete Style Space](https://mlsanity.com/paper/2511.10555/) (2025)
- [VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-On](https://mlsanity.com/paper/2603.11734/) (2026)
- [OmniDiT: Extending Diffusion Transformer to Omni-VTON Framework](https://mlsanity.com/paper/2603.19643/) (2026)
- [ModaFlow: Modality-Aware Flow Matching for High-Fidelity Virtual Try-On](https://mlsanity.com/paper/2606.27773/) (2026)
- [Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis](https://mlsanity.com/paper/2508.04551/) (2025)
- [InstructVTON: Optimal Auto-Masking and Natural-Language-Guided Interactive Style Control for Inpainting-Based Virtual Try-On](https://mlsanity.com/paper/2509.20524/) (2025)
- [ART-VITON: Measurement-Guided Latent Diffusion for Artifact-Free Virtual Try-On](https://mlsanity.com/paper/2509.25749/) (2025)
- [VTONGuard: Automatic Detection and Authentication of AI-Generated Virtual Try-On Content](https://mlsanity.com/paper/2601.13951/) (2026)
- [MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On](https://mlsanity.com/paper/2606.11148/) (2026)

## Abstract (arXiv)

Image-based virtual try-on is an increasingly popular and important task to generate realistic try-on images of the specific person. Recent methods model virtual try-on as image mask-inpaint task, which requires masking the person image and results in significant loss of spatial information. Especially, for in-the-wild try-on scenarios with complex poses and occlusions, mask-based methods often introduce noticeable artifacts. Our research found that a mask-free approach can fully leverage spatial and lighting information from the original person image, enabling high-quality virtual try-on. Consequently, we propose a novel training paradigm for a mask-free try-on diffusion model. We ensure the model's mask-free try-on capability by creating high-quality pseudo-data and further enhance its handling of complex spatial information through effective in-the-wild data augmentation. Besides, a try-on localization loss is designed to concentrate on try-on area while suppressing garment features in non-try-on areas, ensuring precise rendering of garments and preservation of fore/back-ground. In the end, we introduce BooW-VTON, the mask-free virtual try-on diffusion model, which delivers SOTA try-on quality without parsing cost. Extensive qualitative and quantitative experiments have demonstrated superior performance in wild scenarios with such a low-demand input.