UniPortrait：一個統一框架，用於保持身份的單人和多人圖像個性化。

UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization

August 12, 2024

作者: Junjie He, Yifeng Geng, Liefeng Bo

cs.AI

摘要

本文介紹了UniPortrait，一個創新的人像個性化框架，結合了單一和多個ID的定制，具有高度面部保真度、廣泛的面部可編輯性、自由形式輸入描述和多樣的佈局生成。UniPortrait僅由兩個即插即用的模塊組成：ID嵌入模塊和ID路由模塊。ID嵌入模塊通過解耦策略提取多功能可編輯的面部特徵，為每個ID將其嵌入擴散模型的上下文空間。然後，ID路由模塊將這些嵌入組合並自適應地分發到合成圖像中的各自區域，實現單一和多個ID的定制。通過精心設計的兩階段訓練方案，UniPortrait在單一和多個ID的定制方面實現了卓越性能。定量和定性實驗證明了我們方法相對於現有方法的優勢以及其良好的可擴展性，例如與現有生成控制工具的通用兼容性。項目頁面位於https://aigcdesigngroup.github.io/UniPortrait-Page/。

English

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/ .