Hybrid Diffusion Framework for Realistic Virtual Garment Try-On

Hybrid Diffusion Framework for Realistic Virtual Garment Try-On | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hybrid Diffusion Framework for Realistic Virtual Garment Try-On Veerababu Reddy, Pravallika Bhosale, Devi Sahasra Vellalacheruvu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9234367/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Image-based virtual try-on (VTON) has emerged as a pivotal challenge in visual computing, aiming to realistically depict individuals wearing target garments while preserving structural alignment and visual consistency. Recent diffusion-based generative models have shown promise in image synthesis; however, challenges persist in maintaining garment texture fidelity, pose coherence, and stable synthesis quality. This study introduces IMAGDressing, a diffusion-driven VTON framework that integrates pretrained latent diffusion models with pose-guided and garment-conditioning strategies. The framework combines garment feature encoding, human pose estimation, and attention-based conditioning within a frozen denoising backbone to enhance garment alignment and perceptual realism without extensive task-specific retraining. Experimental evaluations on VTON benchmark datasets demonstrate competitive visual quality and consistent garment preservation, with an FID of 8.54, SSIM of 0.90, and LPIPS of 0.07 on the VITON dataset, and an FID of 9.58, SSIM of 0.89, and LPIPS of 0.07 on the VITON HD dataset. Here, we show that diffusion-based conditioning mechanisms offer a viable path for controllable virtual try-on generation, highlighting practical considerations for scalable visual computing applications.The source code, pretrained models, and implementation details are publicly accessible via the GitHub repository: \href{https://github.com/Sahasra75/IMAGDressing-VTON}{https://github.com/Sahasra75/IMAGDressing-VTON}, with a permanently archived and citable version available at Zenodo DOI: \href{https://doi.org/10.5281/zenodo.19232693}{ https://doi.org/10.5281/zenodo.19232693}. Virtual Try-On Diffusion-Based Image Synthesis Garment Conditioning Pose-Guided Generation Visual Computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9234367","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613696030,"identity":"1d427700-3bbc-4da6-9f4e-fe74852bd567","order_by":0,"name":"Veerababu 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Recent diffusion-based generative models have shown promise in image synthesis; however, challenges persist in maintaining garment texture fidelity, pose coherence, and stable synthesis quality. This study introduces IMAGDressing, a diffusion-driven VTON framework that integrates pretrained latent diffusion models with pose-guided and garment-conditioning strategies. The framework combines garment feature encoding, human pose estimation, and attention-based conditioning within a frozen denoising backbone to enhance garment alignment and perceptual realism without extensive task-specific retraining. Experimental evaluations on VTON benchmark datasets demonstrate competitive visual quality and consistent garment preservation, with an FID of 8.54, SSIM of 0.90, and LPIPS of 0.07 on the VITON dataset, and an FID of 9.58, SSIM of 0.89, and LPIPS of 0.07 on the VITON HD dataset. 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