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StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On
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Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task.The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON outperforms the baselines in qualitative and quantitative evaluation, showing promising quality in arbitrary person images. Our code is available at https://github.com/rlawjdghek/StableVITON.
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Cited by 7 Pith papers
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Video Virtual Try-on with Conditional Diffusion Transformer Inpainter
ViTI reformulates video virtual try-on as conditional video inpainting with a full 3D attention diffusion transformer, and reports the best VFID score on VVT (2.121).
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FashionDPO:Fine-tune Fashion Outfit Generation Model using Direct Preference Optimization
FashionDPO applies direct preference optimization with quality, compatibility, and personalization feedback to a fashion diffusion model, reporting improved diversity and alignment on iFashion and Polyvore-U.
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DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On
A training-free virtual try-on pipeline that blends DDIM-inverted garment latents into masked model latents, guided by a lightweight CNN apparel mask.
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SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models
SwiftTry makes diffusion-based video virtual try-on faster and more consistent by shifting non-overlapping video chunks during sampling and caching features across denoising steps.
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Dynamic Try-On: Taming Video Virtual Try-on with Dynamic Attention Mechanism
A DiT-based video try-on framework that reuses the backbone as garment encoder and uses limb-aware dynamic attention to improve temporal consistency.
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Try-On-Adapter: A Simple and Flexible Try-On Paradigm
A diffusion-based adapter performs virtual try-on as outpainting from a reference face and garment, reporting FID 5.56 and 7.23 on VITON-HD.
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TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On
TED-VITON adapts a transformer-based diffusion model (SD3) for virtual try-on with a garment adapter, a text-preservation loss, and LLM-generated prompts, achieving top scores on VITON-HD and DressCode.
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