IMAGDressing-v1: Customizable Virtual Dressing
Latest advances have achieved realistic virtual try-on (VTON) through
localized garment inpainting using latent diffusion models, significantly
enhancing consumers' online shopping experience. However, existing VTON
technologies neglect the need for merchants to showcase garments
comprehensively, including flexible control over garments, optional faces,
poses, and scenes. To address this issue, we define a virtual dressing (VD)
task focused on generating freely editable human images with fixed garments and
optional conditions. Meanwhile, we design a comprehensive affinity metric index
(CAMI) to evaluate the consistency between generated images and reference
garments. Then, we propose IMAGDressing-v1, which incorporates a garment UNet
that captures semantic features from CLIP and texture features from VAE. We
present a hybrid attention module, including a frozen self-attention and a
trainable cross-attention, to integrate garment features from the garment UNet
into a frozen denoising UNet, ensuring users can control different scenes
through text. IMAGDressing-v1 can be combined with other extension plugins,
such as ControlNet and IP-Adapter, to enhance the diversity and controllability
of generated images. Furthermore, to address the lack of data, we release the
interactive garment pairing (IGPair) dataset, containing over 300,000 pairs of
clothing and dressed images, and establish a standard pipeline for data
assembly. Extensive experiments demonstrate that our IMAGDressing-v1 achieves
state-of-the-art human image synthesis performance under various controlled
conditions. The code and model will be available at
https://github.com/muzishen/IMAGDressing.
669882b8be11b410b821c2a8
Fei Shen
claimed_verified
2025-07-28T08:48:18.488Z
64982b945551ac0d48d1b340
/avatars/cd1178d731d0375da05248a32f0e67b7.svg
Fei Shen
user
feishen29
669882b8be11b410b821c2a9
Xin Jiang
669882b8be11b410b821c2aa
Xin He
669882b8be11b410b821c2ab
Hu Ye
669882b8be11b410b821c2ac
Cong Wang
669882b8be11b410b821c2ad
Xiaoyu Du
669882b8be11b410b821c2ae
Zechao Li
669882b8be11b410b821c2af
Jinghui Tang
IMAGDressing-v1: Customizable Virtual Dressing
Latest advances have achieved realistic virtual try-on (VTON) through
localized garment inpainting using latent diffusion models, significantly
enhancing consumers' online shopping experience. However, existing VTON
technologies neglect the need for merchants to showcase garments
comprehensively, including flexible control over garments, optional faces,
poses, and scenes. To address this issue, we define a virtual dressing (VD)
task focused on generating freely editable human images with fixed garments and
optional conditions. Meanwhile, we design a comprehensive affinity metric index
(CAMI) to evaluate the consistency between generated images and reference
garments. Then, we propose IMAGDressing-v1, which incorporates a garment UNet
that captures semantic features from CLIP and texture features from VAE. We
present a hybrid attention module, including a frozen self-attention and a
trainable cross-attention, to integrate garment features from the garment UNet
into a frozen denoising UNet, ensuring users can control different scenes
through text. IMAGDressing-v1 can be combined with other extension plugins,
such as ControlNet and IP-Adapter, to enhance the diversity and controllability
of generated images. Furthermore, to address the lack of data, we release the
interactive garment pairing (IGPair) dataset, containing over 300,000 pairs of
clothing and dressed images, and establish a standard pipeline for data
assembly. Extensive experiments demonstrate that our IMAGDressing-v1 achieves
state-of-the-art human image synthesis performance under various controlled
conditions. The code and model will be available at
https://github.com/muzishen/IMAGDressing.
IMAGDressing-v1 enhances virtual try-on by generating editable human images with fixed garments using a garment UNet, hybrid attention module, and CLIP and VAE features, achieving state-of-the-art performance.
latent diffusion models VTON localized garment inpainting virtual dressing (VD) task affinity metric index (CAMI) garment UNet semantic features texture features VAE hybrid attention module frozen self-attention trainable cross-attention denoising UNet ControlNet IP-Adapter interactive garment pairing (IGPair) dataset human image synthesis