AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent
  Diffusion Models
Authors: name: Xinghui Li; name: Qichao Sun; name: Pengze Zhang; name: Fulong Ye; name: Zhichao Liao; name: Wanquan Feng; name: Songtao Zhao; name: Qian He
Published: 2024-12-05 13:16:47

Abstract: 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.