Papers › StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

4 Dec 2023CVPR 2024 1arXiv:2312.01725archive 2025-07-28

Jeongho Kim, Gyojung Gu, Minho Park, Sunghyun Park, Jaegul Choo

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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imread rlawjdghek/stableviton/dataset.py official repository ran no licence file found · pointer only · c45d0ed437091615 · report
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Tasks

Semantic correspondenceVirtual Try-on

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Virtual Try-on VITON-HD StableVITON FID 8.233 #3 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

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