用自参考图实现个性化的面部年龄转换，更真实保留个人特征。
SelfAge: Personalized Facial Age Transformation Using Self-reference Images
- 基于扩散模型，利用3-5张本人不同时期照片进行个性化微调。
- 在保持身份一致性的前提下，生成自然且符合个体特征的年龄变化图像。
- 适合需要高保真人脸年龄编辑的应用，如影视特效、数字人设计。
面部年龄转换技术旨在改变人物外貌的年龄特征，同时保持身份不变。现有深度学习方法虽能生成自然的年龄变化效果，但仅反映平均趋势，无法捕捉受个人生活经历影响的独特外观。本文提出首个基于扩散模型的个性化年龄转换方法。该模型输入一张人脸图像和目标年龄，输出经年龄编辑的人脸图像。为体现个体特异性，引入同一人不同时期的自参考图像作为额外监督信号，通过约3至5张自参考图像对预训练扩散模型进行微调。此外，设计有效提示词以提升年龄编辑与身份保持性能。实验表明，本方法在定量与定性评估上均优于现有方法。代码与预训练模型已开源：https://github.com/shiiiijp/SelfAge。
原文摘要 · Abstract (English)
Age transformation of facial images is a technique that edits age-related person's appearances while preserving the identity. Existing deep learning-based methods can reproduce natural age transformations; however, they only reproduce averaged transitions and fail to account for individual-specific appearances influenced by their life histories. In this paper, we propose the first diffusion model-based method for personalized age transformation. Our diffusion model takes a facial image and a target age as input and generates an age-edited face image as output. To reflect individual-specific features, we incorporate additional supervision using self-reference images, which are facial images of the same person at different ages. Specifically, we fine-tune a pretrained diffusion model for personalized adaptation using approximately 3 to 5 self-reference images. Additionally, we design an effective prompt to enhance the performance of age editing and identity preservation. Experiments demonstrate that our method achieves superior performance both quantitatively and qualitatively compared to existing methods. The code and the pretrained model are available at https://github.com/shiiiijp/SelfAge.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。