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/ .
Code (1)
Tasks
Layout GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Concat-ID: Towards Universal Identity-Preserving Video Synthesis
We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs Variational Autoencoders to extract image features, which are concatenated with video latents along the sequence dimen…
Human-Domain Subject-to-VideoVideo GenerationVirtual Try-onSIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation
We present SIGMA-GEN, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-GEN is the first to enable single-pass multi-subject identity-preserved generation guided by both s…
Image GenerationVanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision
We present Vanast, a unified framework that generates garment-transferred human animation videos directly from a single human image, garment images, and a pose guidance video. Conventional two-stage pipelines treat image…
Virtual Try-onLearning Joint ID-Textual Representation for ID-Preserving Image Synthesis
We propose a novel framework for ID-preserving generation using a multi-modal encoding strategy rather than injecting identity features via adapters into pre-trained models. Our method treats identity and text as a unifi…
Image GenerationVera: Identity-Faithful Human Subject-to-Video Generation
Subject-to-video (S2V) generation has made substantial progress in preserving reference subjects across diverse categories, yet generic subject consistency remains insufficient for human-centric generation. A video may a…
Video Generation