AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent
  Diffusion Models
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.
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Xinghui Li
claimed_verified
2024-12-06T10:37:49.071Z
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Xinghui Li
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Crayon-Shinchan
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Qichao Sun
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2024-12-06T17:25:33.932Z
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Qichao Sun
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Pengze Zhang
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Fulong Ye
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2024-12-06T17:25:28.019Z
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fulong ye
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Alon77777
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Zhichao Liao
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2024-12-06T17:25:22.068Z
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zhichao liao
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zhichaoCoddy
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Wanquan Feng
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2024-12-06T17:25:06.334Z
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Wanquan Feng
user
wanquan
6752cf141a15db83fd3777f5
Songtao Zhao
6752cf141a15db83fd3777f6
Qian He
admin_assigned
2024-12-06T17:25:59.950Z
645dcad7a19f3e64bbf35e6c
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rV1uHDSnZv7jAvFq4ftj4.jpeg
Qian He
user
heqian
AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent
  Diffusion Models
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.
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.
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