title: OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable
    Virtual Try-on
summary: Image-based virtual try-on (VTON), which aims to generate an outfitted image
  of a target human wearing an in-shop garment, is a challenging image-synthesis
  task calling for not only high fidelity of the outfitted human but also full
  preservation of garment details. To tackle this issue, we propose Outfitting
  over Try-on Diffusion (OOTDiffusion), leveraging the power of pretrained latent
  diffusion models and designing a novel network architecture for realistic and
  controllable virtual try-on. Without an explicit warping process, we propose an
  outfitting UNet to learn the garment detail features, and merge them with the
  target human body via our proposed outfitting fusion in the denoising process
  of diffusion models. In order to further enhance the controllability of our
  outfitting UNet, we introduce outfitting dropout to the training process, which
  enables us to adjust the strength of garment features through classifier-free
  guidance. Our comprehensive experiments on the VITON-HD and Dress Code datasets
  demonstrate that OOTDiffusion efficiently generates high-quality outfitted
  images for arbitrary human and garment images, which outperforms other VTON
  methods in both fidelity and controllability, indicating an impressive
  breakthrough in virtual try-on. Our source code is available at
  https://github.com/levihsu/OOTDiffusion.
paper_authors._id: 65e6ccfd26cbe9fbd260d28a
paper_authors.name: Yuhao Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-07T09:18:49.387Z
paper_authors._id: 64469dde90b3fae03880344e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64469dde90b3fae03880344e/VnLVt_NG3pAjWz0lRoaXm.jpeg
paper_authors.fullname: Yuhao Xu
paper_authors.type: user
paper_authors.user: levihsu
paper_authors._id: 65e6ccfd26cbe9fbd260d28b
paper_authors.name: Tao Gu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-07T09:15:51.316Z
paper_authors._id: 64d4a72884dc227f34003401
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64d4a72884dc227f34003401/YCSNTZD7-uQ3tZ_hMC9-M.jpeg
paper_authors.fullname: Tao Gu
paper_authors.type: user
paper_authors.user: GT2048
paper_authors._id: 65e6ccfd26cbe9fbd260d28c
paper_authors.name: Weifeng Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-07T09:15:54.212Z
paper_authors._id: 6489541e182a613250f21426
paper_authors.avatarUrl: /avatars/837053b14b35bd3b5c37649166d94374.svg
paper_authors.fullname: WEIFENG
paper_authors.type: user
paper_authors.user: ShineChen1024
paper_authors._id: 65e6ccfd26cbe9fbd260d28d
paper_authors.name: Chengcai Chen
paper_title: OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable
    Virtual Try-on
paper_summary: Image-based virtual try-on (VTON), which aims to generate an outfitted image
  of a target human wearing an in-shop garment, is a challenging image-synthesis
  task calling for not only high fidelity of the outfitted human but also full
  preservation of garment details. To tackle this issue, we propose Outfitting
  over Try-on Diffusion (OOTDiffusion), leveraging the power of pretrained latent
  diffusion models and designing a novel network architecture for realistic and
  controllable virtual try-on. Without an explicit warping process, we propose an
  outfitting UNet to learn the garment detail features, and merge them with the
  target human body via our proposed outfitting fusion in the denoising process
  of diffusion models. In order to further enhance the controllability of our
  outfitting UNet, we introduce outfitting dropout to the training process, which
  enables us to adjust the strength of garment features through classifier-free
  guidance. Our comprehensive experiments on the VITON-HD and Dress Code datasets
  demonstrate that OOTDiffusion efficiently generates high-quality outfitted
  images for arbitrary human and garment images, which outperforms other VTON
  methods in both fidelity and controllability, indicating an impressive
  breakthrough in virtual try-on. Our source code is available at
  https://github.com/levihsu/OOTDiffusion.
paper_ai_summary: OOTDiffusion uses latent diffusion models and a novel outfitting UNet to generate high-fidelity and controllable virtual try-on images with preserved garment details.
paper_ai_keywords: latent diffusion models outfitting UNet denoising process diffusion models outfitting fusion outfitting dropout classifier-free guidance