  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.
