title: OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any
    Person
summary: Virtual Try-On (VTON) has become a transformative technology, empowering
  users to experiment with fashion without ever having to physically try on
  clothing. However, existing methods often struggle with generating
  high-fidelity and detail-consistent results. While diffusion models, such as
  Stable Diffusion series, have shown their capability in creating high-quality
  and photorealistic images, they encounter formidable challenges in conditional
  generation scenarios like VTON. Specifically, these models struggle to maintain
  a balance between control and consistency when generating images for virtual
  clothing trials. OutfitAnyone addresses these limitations by leveraging a
  two-stream conditional diffusion model, enabling it to adeptly handle garment
  deformation for more lifelike results. It distinguishes itself with
  scalability-modulating factors such as pose, body shape and broad
  applicability, extending from anime to in-the-wild images. OutfitAnyone's
  performance in diverse scenarios underscores its utility and readiness for
  real-world deployment. For more details and animated results, please see
  https://humanaigc.github.io/outfit-anyone/.
paper_authors._id: 66a0558352e938fff6d1c1e1
paper_authors.name: Ke Sun
paper_authors._id: 66a0558352e938fff6d1c1e2
paper_authors.name: Jian Cao
paper_authors._id: 66a0558352e938fff6d1c1e3
paper_authors.name: Qi Wang
paper_authors._id: 66a0558352e938fff6d1c1e4
paper_authors.name: Linrui Tian
paper_authors._id: 66a0558352e938fff6d1c1e5
paper_authors.name: Xindi Zhang
paper_authors._id: 66a0558352e938fff6d1c1e6
paper_authors.name: Lian Zhuo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:16:09.015Z
paper_authors._id: 6669b27ec5b27e2d625f047b
paper_authors.avatarUrl: /avatars/15b76f88854b1ae94bd7bbfe729ef305.svg
paper_authors.fullname: Lian Zhuo
paper_authors.type: user
paper_authors.user: Hetiey
paper_authors._id: 66a0558352e938fff6d1c1e7
paper_authors.name: Bang Zhang
paper_authors._id: 66a0558352e938fff6d1c1e8
paper_authors.name: Liefeng Bo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:16:27.650Z
paper_authors._id: 63d0cc736b985b0f25d0412c
paper_authors.avatarUrl: /avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
paper_authors.fullname: Bo
paper_authors.type: user
paper_authors.user: Liefeng
paper_authors._id: 66a0558352e938fff6d1c1e9
paper_authors.name: Wenbo Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:16:42.666Z
paper_authors._id: 65855ee8ebe2be208081e69e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65855ee8ebe2be208081e69e/CIDPY0H6v4BiLkcxSANxD.jpeg
paper_authors.fullname: zhouwenbo
paper_authors.type: user
paper_authors.user: bobozi
paper_authors._id: 66a0558352e938fff6d1c1ea
paper_authors.name: Weiming Zhang
paper_authors._id: 66a0558352e938fff6d1c1eb
paper_authors.name: Daiheng Gao
paper_title: OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any
    Person
paper_summary: Virtual Try-On (VTON) has become a transformative technology, empowering
  users to experiment with fashion without ever having to physically try on
  clothing. However, existing methods often struggle with generating
  high-fidelity and detail-consistent results. While diffusion models, such as
  Stable Diffusion series, have shown their capability in creating high-quality
  and photorealistic images, they encounter formidable challenges in conditional
  generation scenarios like VTON. Specifically, these models struggle to maintain
  a balance between control and consistency when generating images for virtual
  clothing trials. OutfitAnyone addresses these limitations by leveraging a
  two-stream conditional diffusion model, enabling it to adeptly handle garment
  deformation for more lifelike results. It distinguishes itself with
  scalability-modulating factors such as pose, body shape and broad
  applicability, extending from anime to in-the-wild images. OutfitAnyone's
  performance in diverse scenarios underscores its utility and readiness for
  real-world deployment. For more details and animated results, please see
  https://humanaigc.github.io/outfit-anyone/.
paper_ai_summary: OutfitAnyone uses a two-stream conditional diffusion model to achieve high-fidelity garment deformation in virtual try-on scenarios, addressing challenges in control and consistency across diverse images and conditions.
paper_ai_keywords: diffusion models Stable Diffusion series conditional generation two-stream conditional diffusion model garment deformation scalability pose body shape in-the-wild images virtual try-on