基于AdaFace优化的两阶段三维人脸精细重建方法  

Two-stage 3D Fine-grained Facial Reconstruction Method Based on AdaFace Optimization

作　　者：马飞 张娟 赵俊莉 MA Fei;ZHANG Juan;ZHAO Jun-li(College of Computer Science and Technology,Qingdao University,Qingdao 266071,China;School of Journalism and Communication,Shaanxi Normal University,Xi'an 710119,China)

机构地区：[1]青岛大学计算机科学技术学院,青岛266071 [2]陕西师范大学新闻与传播学院,西安710119

出　　处：《青岛大学学报（自然科学版）》2024年第3期40-48,共9页Journal of Qingdao University(Natural Science Edition)

基　　金：国家自然科学基金(批准号:62172247,61702293,61772294)资助;山东省自然科学基金(批准号:ZR2019LZH002,ZR2020QF039)资助,陕西省重点研发计划项目(项目编号:2023-YBSF-28)资助。

摘　　要：为解决三维人脸重建方法DECA(Detailed Expression Capture and Animation)采用2D图像训练导致信息缺失所带来的重建形状不够准确和MICA(MetrIC FAce)方法缺乏高频细节以及遇到无法识别的人脸照片重建失败的问题,利用3D数据和更为鲁棒的人脸识别网络完成有监督和自监督混合训练,提出基于FLAME(Fitting Landmarks And Morphable Expression)人脸模型、AdaFace(Quality Adaptive Margin for Face Recognition)人脸识别网络和DECA框架的高精度细节融合两阶段人脸重建方法(FIne-grained Facial Reconstruction,FiFR)。在粗重建阶段通过Adaface身份编码器将2D图像编码至隐空间,由2D和3D数据训练的映射网络将编码转化为FLAME人脸模型的相关参数,生成粗重建结果;在精细重建阶段,参考DECA方法,通过细节一致性损失约束生成详细的UV置换贴图,增强人脸的高频细节,实现了单一图像的三维人脸精细重建。实验结果表明,FiFR比DECA方法重建结果平均误差减少了14%,针对低分辨率图像误差减少达到了18%;相对于MICA方法,重建人脸具有更多的高频细节。In order to address the limitations of current 3D facial reconstruction methods,such as the inaccuracies stemming from training DECA(Detailed Expression Capture and Animation)on 2D images leading to information loss,and the inability of MICA(MetrIc FAce)to handle high-frequency details and unrecognized facial images,the two-stage facial reconstruction approach termed FIne-grained Facial Reconstruction(FiFR)was suggested,which leverages 3D data and a more robust face recognition network for supervised and self-supervised mixed training.This method integrates the FLAME(Fitting Landmarks and Morphable Expression)facial model,the AdaFace(Quality Adaptive Margin for Face Recognition)face recognition network,and the DECA framework to achieve high-precision detail fusion.In the coarse reconstruction stage,Adaface identity encoders encoded 2D images into latent spaces,and a mapping network trained on 2D and 3D data transformed the encodings into relevant parameters of the FLAME model,generated coarse reconstruction results.In the fine reconstruction stage,inspired by DECA,a detail-consistency loss-constrained UV displacement map was generated to enhance the facial high-frequency details,achieving fine-grained facial reconstruction from a single image.Experimental results demonstrate that Fi-FR reduces the average reconstruction error by 14%compared to DECA,with an 18%reduction in error for low-resolution images.Furthermore,FiFR exhibits more high-frequency details compared to the MICA method.

关 键 词：三维人脸重建 深度学习 神经网络 

分 类 号：TP37[自动化与计算机技术—计算机系统结构]