UniPortrait: A Unified Framework for Identity-Preserving Single- and
  Multi-Human Image Personalization
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/ .
66babb2332be421cd8a79686
Junjie He
claimed_verified
2025-02-06T14:15:42.906Z
63b54949889aa6707f08bfd7
/avatars/52101bb4289ba1e0b33b925a1a9536c0.svg
Junjie He
user
Junjie96
66babb2332be421cd8a79687
Yifeng Geng
admin_assigned
2024-08-13T09:21:07.441Z
659caf6b0030e8faffb00c41
/avatars/dde52bcf694120e59307b4ab8a7eeb33.svg
gengyifeng
user
gengyifeng
66babb2332be421cd8a79688
Liefeng Bo
admin_assigned
2024-08-13T09:21:27.131Z
63d0cc736b985b0f25d0412c
/avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
Bo
user
Liefeng
UniPortrait: A Unified Framework for Identity-Preserving Single- and
  Multi-Human Image Personalization
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/ .
UniPortrait is a framework for high-fidelity human image personalization using diffusion models, featuring both single- and multi-ID customization and diverse layout options.
ID embedding diffusion models decoupling strategy context space ID routing two-stage training generative control tools