title: UniPortrait: A Unified Framework for Identity-Preserving Single- and
    Multi-Human Image Personalization
summary: This paper presents UniPortrait, an innovative human image personalization
  framework that unifies single- and multi-ID customization with high face
  fidelity, extensive facial editability, free-form input description, and
  diverse layout generation. UniPortrait consists of only two plug-and-play
  modules: an ID embedding module and an ID routing module. The ID embedding
  module extracts versatile editable facial features with a decoupling strategy
  for each ID and embeds them into the context space of diffusion models. The ID
  routing module then combines and distributes these embeddings adaptively to
  their respective regions within the synthesized image, achieving the
  customization of single and multiple IDs. With a carefully designed two-stage
  training scheme, UniPortrait achieves superior performance in both single- and
  multi-ID customization. Quantitative and qualitative experiments demonstrate
  the advantages of our method over existing approaches as well as its good
  scalability, e.g., the universal compatibility with existing generative control
  tools. The project page is at
  https://aigcdesigngroup.github.io/UniPortrait-Page/ .
paper_authors._id: 66babb2332be421cd8a79686
paper_authors.name: Junjie He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-06T14:15:42.906Z
paper_authors._id: 63b54949889aa6707f08bfd7
paper_authors.avatarUrl: /avatars/52101bb4289ba1e0b33b925a1a9536c0.svg
paper_authors.fullname: Junjie He
paper_authors.type: user
paper_authors.user: Junjie96
paper_authors._id: 66babb2332be421cd8a79687
paper_authors.name: Yifeng Geng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:21:07.441Z
paper_authors._id: 659caf6b0030e8faffb00c41
paper_authors.avatarUrl: /avatars/dde52bcf694120e59307b4ab8a7eeb33.svg
paper_authors.fullname: gengyifeng
paper_authors.type: user
paper_authors.user: gengyifeng
paper_authors._id: 66babb2332be421cd8a79688
paper_authors.name: Liefeng Bo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:21:27.131Z
paper_authors._id: 63d0cc736b985b0f25d0412c
paper_authors.avatarUrl: /avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
paper_authors.fullname: Bo
paper_authors.type: user
paper_authors.user: Liefeng
paper_title: UniPortrait: A Unified Framework for Identity-Preserving Single- and
    Multi-Human Image Personalization
paper_summary: This paper presents UniPortrait, an innovative human image personalization
  framework that unifies single- and multi-ID customization with high face
  fidelity, extensive facial editability, free-form input description, and
  diverse layout generation. UniPortrait consists of only two plug-and-play
  modules: an ID embedding module and an ID routing module. The ID embedding
  module extracts versatile editable facial features with a decoupling strategy
  for each ID and embeds them into the context space of diffusion models. The ID
  routing module then combines and distributes these embeddings adaptively to
  their respective regions within the synthesized image, achieving the
  customization of single and multiple IDs. With a carefully designed two-stage
  training scheme, UniPortrait achieves superior performance in both single- and
  multi-ID customization. Quantitative and qualitative experiments demonstrate
  the advantages of our method over existing approaches as well as its good
  scalability, e.g., the universal compatibility with existing generative control
  tools. The project page is at
  https://aigcdesigngroup.github.io/UniPortrait-Page/ .
paper_ai_summary: UniPortrait is a framework for high-fidelity human image personalization using diffusion models, featuring both single- and multi-ID customization and diverse layout options.
paper_ai_keywords: ID embedding diffusion models decoupling strategy context space ID routing two-stage training generative control tools