基于深度学习的单幅图像三维人脸重建研究综述

Review of 3D Face Reconstruction Based on Single Image

在计算机视觉领域中,三维人脸重建是一个具有研究价值的方向,高质量地重建出三维人脸在人脸识别、防伪、游戏娱乐、影视动画和美容医疗等领域具有重要的意义。近二十年来,虽然基于单幅图像的三维人脸重建领域已经取得很大的进展,但使用传统算法进行重建的结果仍会受到人脸表情、遮挡、环境光的影响,并且会出现重建效果精度不佳和鲁棒性不足等问题。随着深度学习进入三维人脸重建领域,各种优于传统重建算法的方法相继出现。文中首先重点介绍了基于深度学习的单幅图像三维人脸重建算法,将算法按不同的网络架构分为4类,并对各类最具有代表性的方法进行了详细阐述。然后汇总了基于单幅图像的三维人脸重建算法常用的三维人脸数据集,并在数据集上对具有代表性的方法进行了性能评估。最后对基于单幅图像的三维人脸重建领域进行了总结与展望。

In the field of computer vision,3 D face reconstruction is a valuable research direction.High quality reconstruction of 3 D faces can find applications in face recognition,anti-proofing,animation and medical cosmetology.In the last two decades,although great progress has been made 3 D face reconstruction based on a single image,the results of reconstruction using traditionalalgorithms are still facing the challenge of facial expression,occlusion and ambient light,and there will be problems such as poor reconstruction accuracy and robustness.With the rapid development of deep learning in 3 D face reconstruction,various methods which are superior to traditional reconstruction algorithms have emerged.Firstly,this paper focuses on deep-learning-based reconstruction algorithms.The algorithms are divided into four categories according to different network architecture,and the most popular methods are described in detail.Then commonly used 3 D face data sets are introduced,and performance of representative methods are evaluated.Finally,conclusions and prospects of the single-image-based 3 D face reconstruction are given.