Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. To understand the development of frontal face generation models and grasp the current research hotspots and trends, existing methods based on 3D models, deep learning, and hybrid models are summarized, and the current commonly used face generation methods are introduced. Dataset, and compare the performance of existing models through experiments. The purpose of this paper is to fundamentally understand the advantages of existing frontal face generation, sort out the key issues of such generation, and look toward future development trends.

中文翻译：

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多视角正面图像生成：一项调查

不同视角的人脸图像降低了人脸识别的准确率，而正面人脸图像的生成是人脸识别领域的重要研究课题。为了了解正面人脸生成模型的发展，把握当前的研究热点和趋势，总结了基于3D模型、深度学习和混合模型的现有方法，并介绍了当前常用的人脸生成方法。数据集，并通过实验比较现有模型的性能。本文的目的是从根本上认识现有正面生成的优势，梳理该生成的关键问题，并展望未来的发展趋势。