数字人脸渲染与外观恢复方法综述  

Survey of digital face rendering and appearance recovery methods

作　　者：郝琮晖 杜悠扬 王璐 王贝贝 Hao Conghui;Du Youyang;Wang Lu;Wang Beibei(College of Computer Science,Nankai University,Tianjin 300350,China;School of Software,Shandong University,Jinan 250101,China;School of Intelligence Science and Technology,Nanjing University,Suzhou 215163,China)

机构地区：[1]南开大学计算机学院,天津300350 [2]山东大学软件学院,济南250101 [3]南京大学智能科学与技术学院,苏州215163

出　　处：《中国图象图形学报》2024年第9期2513-2540,共28页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62172220);新一代人工智能国家科技重大专项(2022ZD0116305)。

摘　　要：数字人技术引起了数字孪生、元宇宙等领域的广泛关注,其中人脸作为数字人的重要构成部分,其数字化生成和呈现成为人们关注的焦点,且相关技术已经在电影、游戏等领域得到了广阔应用。人们对实现逼真的人脸效果以及精确恢复人脸的需求日益增长,但由于人脸的多层材质结构、复杂的半透明皮肤效果以及毛孔、褶皱等微观特征的综合影响,实现高保真的、高效的人脸渲染一直是领域内的难题。此外,通过采集设备对人脸的几何和外观进行恢复是构建人脸数据的重要方式,然而对人脸的高品质恢复也同样受限于高成本的采集设备和相关数据集的不足。本文对数字人脸的渲染与恢复的相关方法进行综述。首先介绍了真实感人脸的渲染方法,根据其不同的渲染原理,将它们分为基于扩散近似的渲染方法和基于蒙特卡洛采样的渲染方法,并着重分析了基于近似扩散渲染方法的发展现状及面临的问题。进一步,将人脸恢复工作分类为基于专业采集设备的高精度恢复和基于深度学习的低精度恢复。针对高精度人脸恢复,从主动照明和被动捕获两个分支,对相应的工作进行了总结。针对结合深度学习的低精度人脸恢复方法,将其分类为几何细节的恢复、纹理贴图的恢复以及人脸材质信息的恢复3个方面进行介绍。本文系统地论述了各类方法的核心思路,并进行了横向对比和分析。最后,对未来人脸渲染及恢复方法的发展趋势进行了展望。希望本文可以为人脸渲染和外观恢复的初学者提供一些背景知识和思路启发。Digital human technology has attracted widespread attention in digital twins and metaverse fields.As an integral part of digital humans,people have started focusing on facial digitization and presentation.Consequently,the associated techniques find extensive applications in film,gaming,and virtual reality.A growing demand for facial realism rendering and high-quality facial inverse recovery has been observed.However,given the complex and multilayered material structure of the face,facial realism rendering presents a challenge.Furthermore,the composition of internal skin chemicals,such as melanin and hemoglobin,highly influences skin rendering.Factors,such as temperature and blood flow rate,may influence the skin’s appearance.The semitransparency of the skin introduces difficulties in the simulation of subsurface scattering effects,in addition to the wide presence of microscopic geometric features,such as pores and wrinkles on the face.All the issues mentioned above cause problems in the rendering domain and raise the demand for the quality of facial recovery.In addition,as a result of people’s exposure to real human faces in daily life,a heightened sensitivity to the texture and details of digital human faces has been observed,and this condition places greater demands on their realism and accuracy.Meanwhile,recovery of facial geometry and appearance is a crucial method for the construction of facial datasets.However,the high costs of acquisition equipment often constrain high-quality facial recovery,and most studies are limited by the acquisition speed for facial data,which result in the challenging capture of dynamic facial appearance.Lightweight recovery methods also encounter challenges related to the lack of facial material datasets.This paper presents an overview of recent advances in rendering and recovery of digital human faces.First,we introduce methods for realistic facial rendering and categorize them based on diffusion approximation and Monte Carlo approaches.Methods based on diffusion approximatio

关 键 词：人脸真实感渲染 次表面散射 人脸逆向恢复 主动照明 被动捕获 深度学习 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]