UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization
<|reference_start|>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/ .<|reference_end|>
@article{he2024uniportrait:,
    title={UniPortrait: A Unified Framework for Identity-Preserving Single- and
  Multi-Human Image Personalization},
    author={Junjie He, Yifeng Geng, Liefeng Bo},
    journal={arXiv preprint arXiv:2408.05939},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05939},
    primaryClass={cs.CV}
}