title: AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent
    Diffusion Models
summary: Recent advances in garment-centric image generation from text and image
  prompts based on diffusion models are impressive. However, existing methods
  lack support for various combinations of attire, and struggle to preserve the
  garment details while maintaining faithfulness to the text prompts, limiting
  their performance across diverse scenarios. In this paper, we focus on a new
  task, i.e., Multi-Garment Virtual Dressing, and we propose a novel AnyDressing
  method for customizing characters conditioned on any combination of garments
  and any personalized text prompts. AnyDressing comprises two primary networks
  named GarmentsNet and DressingNet, which are respectively dedicated to
  extracting detailed clothing features and generating customized images.
  Specifically, we propose an efficient and scalable module called
  Garment-Specific Feature Extractor in GarmentsNet to individually encode
  garment textures in parallel. This design prevents garment confusion while
  ensuring network efficiency. Meanwhile, we design an adaptive
  Dressing-Attention mechanism and a novel Instance-Level Garment Localization
  Learning strategy in DressingNet to accurately inject multi-garment features
  into their corresponding regions. This approach efficiently integrates
  multi-garment texture cues into generated images and further enhances
  text-image consistency. Additionally, we introduce a Garment-Enhanced Texture
  Learning strategy to improve the fine-grained texture details of garments.
  Thanks to our well-craft design, AnyDressing can serve as a plug-in module to
  easily integrate with any community control extensions for diffusion models,
  improving the diversity and controllability of synthesized images. Extensive
  experiments show that AnyDressing achieves state-of-the-art results.
paper_authors._id: 6752cf141a15db83fd3777ef
paper_authors.name: Xinghui Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T10:37:49.071Z
paper_authors._id: 6752cd83ffaeeb979db974ae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ci915Tdmv3uWbCqGy7LqL.png
paper_authors.fullname: Xinghui Li
paper_authors.type: user
paper_authors.user: Crayon-Shinchan
paper_authors._id: 6752cf141a15db83fd3777f0
paper_authors.name: Qichao Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-06T17:25:33.932Z
paper_authors._id: 666a6e144a6c703a09d787d5
paper_authors.avatarUrl: /avatars/ae5dfa87e352a3561cd0cc53d5e3a91a.svg
paper_authors.fullname: Qichao Sun
paper_authors.type: user
paper_authors.user: giruhc9gj
paper_authors._id: 6752cf141a15db83fd3777f1
paper_authors.name: Pengze Zhang
paper_authors._id: 6752cf141a15db83fd3777f2
paper_authors.name: Fulong Ye
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-06T17:25:28.019Z
paper_authors._id: 6339029a76421c0543167075
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6339029a76421c0543167075/3npT0NxTMV-MLWDThyBy8.png
paper_authors.fullname: fulong ye
paper_authors.type: user
paper_authors.user: Alon77777
paper_authors._id: 6752cf141a15db83fd3777f3
paper_authors.name: Zhichao Liao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-06T17:25:22.068Z
paper_authors._id: 664820aef16e33cc99e57a4c
paper_authors.avatarUrl: /avatars/f27bf907ddc98a45f29554393bfeabbd.svg
paper_authors.fullname: zhichao liao
paper_authors.type: user
paper_authors.user: zhichaoCoddy
paper_authors._id: 6752cf141a15db83fd3777f4
paper_authors.name: Wanquan Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-06T17:25:06.334Z
paper_authors._id: 64462aec86679b753ff7ece0
paper_authors.avatarUrl: /avatars/ff0e1e693b984c9d569dc462fa7fe4ef.svg
paper_authors.fullname: Wanquan Feng
paper_authors.type: user
paper_authors.user: wanquan
paper_authors._id: 6752cf141a15db83fd3777f5
paper_authors.name: Songtao Zhao
paper_authors._id: 6752cf141a15db83fd3777f6
paper_authors.name: Qian He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-06T17:25:59.950Z
paper_authors._id: 645dcad7a19f3e64bbf35e6c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rV1uHDSnZv7jAvFq4ftj4.jpeg
paper_authors.fullname: Qian He
paper_authors.type: user
paper_authors.user: heqian
paper_title: AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent
    Diffusion Models
paper_summary: Recent advances in garment-centric image generation from text and image
  prompts based on diffusion models are impressive. However, existing methods
  lack support for various combinations of attire, and struggle to preserve the
  garment details while maintaining faithfulness to the text prompts, limiting
  their performance across diverse scenarios. In this paper, we focus on a new
  task, i.e., Multi-Garment Virtual Dressing, and we propose a novel AnyDressing
  method for customizing characters conditioned on any combination of garments
  and any personalized text prompts. AnyDressing comprises two primary networks
  named GarmentsNet and DressingNet, which are respectively dedicated to
  extracting detailed clothing features and generating customized images.
  Specifically, we propose an efficient and scalable module called
  Garment-Specific Feature Extractor in GarmentsNet to individually encode
  garment textures in parallel. This design prevents garment confusion while
  ensuring network efficiency. Meanwhile, we design an adaptive
  Dressing-Attention mechanism and a novel Instance-Level Garment Localization
  Learning strategy in DressingNet to accurately inject multi-garment features
  into their corresponding regions. This approach efficiently integrates
  multi-garment texture cues into generated images and further enhances
  text-image consistency. Additionally, we introduce a Garment-Enhanced Texture
  Learning strategy to improve the fine-grained texture details of garments.
  Thanks to our well-craft design, AnyDressing can serve as a plug-in module to
  easily integrate with any community control extensions for diffusion models,
  improving the diversity and controllability of synthesized images. Extensive
  experiments show that AnyDressing achieves state-of-the-art results.
paper_ai_summary: AnyDressing enhances garment-centric image generation by integrating garment-specific features and personalized text prompts using GarmentsNet and DressingNet, achieving state-of-the-art results in multi-garment virtual dressing.
paper_ai_keywords: diffusion models Multi-Garment Virtual Dressing AnyDressing GarmentsNet DressingNet Garment-Specific Feature Extractor Dressing-Attention mechanism Instance-Level Garment Localization Learning Garment-Enhanced Texture Learning