title: FitDiT: Advancing the Authentic Garment Details for High-fidelity
    Virtual Try-on
summary: Although image-based virtual try-on has made considerable progress, emerging
  approaches still encounter challenges in producing high-fidelity and robust
  fitting images across diverse scenarios. These methods often struggle with
  issues such as texture-aware maintenance and size-aware fitting, which hinder
  their overall effectiveness. To address these limitations, we propose a novel
  garment perception enhancement technique, termed FitDiT, designed for
  high-fidelity virtual try-on using Diffusion Transformers (DiT) allocating more
  parameters and attention to high-resolution features. First, to further improve
  texture-aware maintenance, we introduce a garment texture extractor that
  incorporates garment priors evolution to fine-tune garment feature,
  facilitating to better capture rich details such as stripes, patterns, and
  text. Additionally, we introduce frequency-domain learning by customizing a
  frequency distance loss to enhance high-frequency garment details. To tackle
  the size-aware fitting issue, we employ a dilated-relaxed mask strategy that
  adapts to the correct length of garments, preventing the generation of garments
  that fill the entire mask area during cross-category try-on. Equipped with the
  above design, FitDiT surpasses all baselines in both qualitative and
  quantitative evaluations. It excels in producing well-fitting garments with
  photorealistic and intricate details, while also achieving competitive
  inference times of 4.57 seconds for a single 1024x768 image after DiT structure
  slimming, outperforming existing methods.
paper_authors._id: 673c23932b49c38cbe85be38
paper_authors.name: Boyuan Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T16:38:21.750Z
paper_authors._id: 63b63a470d5913eee48bc2b3
paper_authors.avatarUrl: /avatars/9be7857a6e06d6cf99bd2829ded9ef02.svg
paper_authors.fullname: jiang
paper_authors.type: user
paper_authors.user: BoyuanJiang
paper_authors._id: 673c23932b49c38cbe85be39
paper_authors.name: Xiaobin Hu
paper_authors._id: 673c23932b49c38cbe85be3a
paper_authors.name: Donghao Luo
paper_authors._id: 673c23932b49c38cbe85be3b
paper_authors.name: Qingdong He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T16:38:47.038Z
paper_authors._id: 64799de9587656b38e8dcf7a
paper_authors.avatarUrl: /avatars/87069f03d44cc2d9e0458bc17bd0159a.svg
paper_authors.fullname: heqingdong
paper_authors.type: user
paper_authors.user: hqd
paper_authors._id: 673c23932b49c38cbe85be3c
paper_authors.name: Chengming Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T16:38:52.876Z
paper_authors._id: 652fab9d04a34a9282bf29d6
paper_authors.avatarUrl: /avatars/cd5967b37ebb1225e9ae1d46f196e2e2.svg
paper_authors.fullname: Chengming Xu
paper_authors.type: user
paper_authors.user: ChengmingX
paper_authors._id: 673c23932b49c38cbe85be3d
paper_authors.name: Jinlong Peng
paper_authors._id: 673c23932b49c38cbe85be3e
paper_authors.name: Jiangning Zhang
paper_authors._id: 673c23932b49c38cbe85be3f
paper_authors.name: Chengjie Wang
paper_authors._id: 673c23932b49c38cbe85be40
paper_authors.name: Yunsheng Wu
paper_authors._id: 673c23932b49c38cbe85be41
paper_authors.name: Yanwei Fu
paper_title: FitDiT: Advancing the Authentic Garment Details for High-fidelity
    Virtual Try-on
paper_summary: Although image-based virtual try-on has made considerable progress, emerging
  approaches still encounter challenges in producing high-fidelity and robust
  fitting images across diverse scenarios. These methods often struggle with
  issues such as texture-aware maintenance and size-aware fitting, which hinder
  their overall effectiveness. To address these limitations, we propose a novel
  garment perception enhancement technique, termed FitDiT, designed for
  high-fidelity virtual try-on using Diffusion Transformers (DiT) allocating more
  parameters and attention to high-resolution features. First, to further improve
  texture-aware maintenance, we introduce a garment texture extractor that
  incorporates garment priors evolution to fine-tune garment feature,
  facilitating to better capture rich details such as stripes, patterns, and
  text. Additionally, we introduce frequency-domain learning by customizing a
  frequency distance loss to enhance high-frequency garment details. To tackle
  the size-aware fitting issue, we employ a dilated-relaxed mask strategy that
  adapts to the correct length of garments, preventing the generation of garments
  that fill the entire mask area during cross-category try-on. Equipped with the
  above design, FitDiT surpasses all baselines in both qualitative and
  quantitative evaluations. It excels in producing well-fitting garments with
  photorealistic and intricate details, while also achieving competitive
  inference times of 4.57 seconds for a single 1024x768 image after DiT structure
  slimming, outperforming existing methods.
paper_ai_keywords: Diffusion Transformers (DiT) garment perception enhancement garment texture extractor garment priors evolution frequency-domain learning frequency distance loss dilated-relaxed mask strategy high-frequency garment details cross-category try-on photorealistic intricate details DiT structure slimming