基于生成对抗网络和扩散模型的人脸年龄编辑综述OA

[Purpose]In recent years,deep generative models have made significant progress in the task of facial age editing.This paper summarizes facial age editing methods based on deep generative models such as Generative Adversarial Networks(GANs)and diffusion models.[Methods]This survey first introduces the basic concepts of face age editing,relevant data-sets,and evaluation metrics.It then analyzes the applications of commonly used GANs,Diffusion Models,and their variants in age editing tasks.The performance of existing models in terms of age accuracy,identity consistency,and image quality is summarized,and the suitability of different evalua-tion metrics is discussed.[Results]Age editing technology based on GANs and Diffusion Models have achieved significant improvements in image quality and age prediction accuracy.However,challenges remain in generating fine details,particularly when dealing with large age gaps.[Conclusions]Future research in face age editing can further enhance model generation capability and application effects by developing larg-er,higher-quality datasets and integrating 3D face reconstruction technology with efficient sampling algo-rithms from Diffusion Models.