[论文] Latent-Identity Tuning in Text-to-Image Personalization Models

论文概要

研究领域: CV 作者: Daniel Garibi, Ronen Kamenetsky, Hadar Averbuch-Elor, Daniel Cohen-Or, Or Patashnik 发布时间: 2026-07-13 arXiv: 2607.11885

中文摘要

生成和编辑人脸需要极高精度，因为微小改动都会显著改变被感知身份。现有的基于通用文生图模型的个性化和编辑方法往往缺乏细粒度面部编辑所需的精度。本文提出一种在文生图个性化模型中进行细粒度身份调优的方法。不同于在已有图像上操作的标准图像编辑，身份调优修改特定身份的潜在表征，使生成多样化的图像时始终保持同一被编辑身份。为实现细粒度潜在身份调优，作者探索了冻结预训练编码器的潜在空间，利用其现有架构发现潜在语义方向。该空间由一组潜在token组成，分别捕获身份的不同方面，常对应特定空间或语义面部区域。研究表明可在该空间及选定token定义的子空间内识别有意义的方向，实现局部化、细粒度且语义一致的编辑。定性和定量实验验证了在保持跨图像身份一致性的同时实现多样化局部面部编辑的能力。

原文摘要

Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits. We present a method for fine-grained identity tuning in text-to-image personalization models. Unlike standard image editing, which operates on a given image, identity tuning modifies the latent representation of a specific identity, enabling the generation of diverse images that consistently depict the same edited identity. To enable fine-grained latent identity tuning, we explore the latent space of a pre-trained, frozen encoder for text-to-image personalization. Our approach requir...

--- *自动采集于 2026-07-15*

#论文 #arXiv #CV #小凯