title: IMAGDressing-v1: Customizable Virtual Dressing
summary: 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.
paper_authors._id: 669882b8be11b410b821c2a8
paper_authors.name: Fei Shen
paper_authors._id: 669882b8be11b410b821c2a9
paper_authors.name: Xin Jiang
paper_authors._id: 669882b8be11b410b821c2aa
paper_authors.name: Xin He
paper_authors._id: 669882b8be11b410b821c2ab
paper_authors.name: Hu Ye
paper_authors._id: 669882b8be11b410b821c2ac
paper_authors.name: Cong Wang
paper_authors._id: 669882b8be11b410b821c2ad
paper_authors.name: Xiaoyu Du
paper_authors._id: 669882b8be11b410b821c2ae
paper_authors.name: Zechao Li
paper_authors._id: 669882b8be11b410b821c2af
paper_authors.name: Jinghui Tang
paper_title: IMAGDressing-v1: Customizable Virtual Dressing
paper_summary: 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.
paper_ai_keywords: localized garment inpainting latent diffusion models virtual dressing (VD) generated freely editable human images comprehensive affinity metric index (CAMI) garment UNet semantic features CLIP texture features VAE hybrid attention module frozen self-attention trainable cross-attention frozen denoising UNet generated images control text ControlNet IP-Adapter interactive garment pairing (IGPair) dataset human image synthesis