LIPE：学习个性化身份先验用于非刚性图像编辑
LIPE: Learning Personalized Identity Prior for Non-rigid Image Editing
AI总结：
本文提出两阶段框架LIPE，通过学习个性化身份先验解决非刚性图像编辑中身份特征缺失导致结果不一致的问题，并在定性和定量实验中超越现有方法。
摘要：
尽管近年来得益于文本到图像扩散模型的显著进展，图像编辑领域取得了重大突破，但非刚性图像编辑问题依然存在其复杂性与挑战性。由于缺乏独特的身份特征，现有方法往往无法获得一致的结果。因此，学习个性化身份先验可能有助于提升编辑结果的一致性。在本文中，我们探索了一项新任务：为基于文本的非刚性图像编辑学习个性化身份先验。为了解决联合学习先验与编辑图像时存在的问题，我们提出了LIPE，这是一个两阶段框架，旨在利用同一主体的有限图像集定制生成模型，随后应用带有学习先验的模型进行非刚性图像编辑。实验结果在定性和定量上证明了我们的方法在各种编辑场景中优于以往相关的领先方法。
英文摘要：
Although recent years have witnessed significant advancements in image editing thanks to the remarkable progress of text-to-image diffusion models, the problem of non-rigid image editing still presents its complexities and challenges. Existing methods often fail to achieve consistent results due to the absence of unique identity characteristics. Thus, learning a personalized identity prior might help with consistency in the edited results. In this paper, we explore a novel task: learning the personalized identity prior for text-based non-rigid image editing. To address the problems in jointly learning prior and editing the image, we present LIPE, a two-stage framework designed to customize the generative model utilizing a limited set of images of the same subject, and subsequently employ the model with learned prior for non-rigid image editing. Experimental results demonstrate the advantages of our approach in various editing scenarios over past related leading methods in qualitative and quantitative ways.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
