LIPE: Learning Personalized Identity Prior for Non-rigid Image Editing

• URL: http://arxiv.org/abs/2406.17236v1
• Date: Tue, 25 Jun 2024 02:56:16 GMT
• Title: LIPE: Learning Personalized Identity Prior for Non-rigid Image Editing
• Authors: Aoyang Liu, Qingnan Fan, Shuai Qin, Hong Gu, Yansong Tang,
• Abstract summary: LIPE is a two-stage framework designed to customize the generative model utilizing a limited set of images of the same subject. 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.
• Score: 20.861672583434718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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