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收稿日期：2023-09-25，
修回日期：2024-04-01，
纸质出版日期：2024-09-16
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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 approximation， which focus on the efficient achievement of the semitransparency effect of the skin， are constrained by strict assumptions and suffer from certain limitations in precision. However， their simplified subsurface scattering models can render satisfactory images relatively quickly. Dynamic and interactive applications， such as games， often apply these methods. On the other hand， methods based on the Monte Carlo approach yield high precision and robust results via the meticulous and comprehensive simulation of the complex interactions between light and skin but require long computation times to converge. In applications， such as movies， where highly realistic visual effects are needed， they often become the preferred choice. We emphasized the development and challenges of methods based on diffusion approximation and divided them into improvements in the diffusion profiles， with real-time implementation of subsurface scattering， and hybrid methods combined with Monte Carlo techniques for detailed discussion. A recent Monte Carlo research aimed at improving the convergence rate for applications in facial rendering， including zero-variance random walks， next-event estimation， and path guiding. Second， we divided facial recovery work into two categories： high-precision recovery based on specialized acquisition equipment and low-precision recovery based on deep learning. This paper further categorizes the former based on the use of specialized lighting equipment， which distinguishes between active illumination and passive capture techniques， with provided detailed explanations for each category. Active illumination relies on professional lighting equipment， such as the application of gradient lighting to recover high-precision normal maps， to improve recovery quality. Conversely， passive capture methods are independent of professional lighting equipment， and any artificially provided lighting is limited to uniform illumination to reduce the interference of scene lighting on recovery and similar auxiliary roles. The exploration also focuses on low-precision facial recovery methods incorporating deep learning and classifies them into three categories， namely， geometric detail， texture mapping， and facial material information recoveries， to provide in-depth insights into each approach. We discuss a strategy for overcoming the limitations of geometric recovery based on parametric models， introduced refined parametric expressions of models， and predicted a range of maps， including displacement maps， that represent the model surface’s geometric details. For texture recovery， we explored the application of deep neural networks in generative tasks in the prediction of high-fidelity and personalized facial skin textures. Comprehensive reviews the various attempts to mitigate the ill-posed problem of separating reflectance information. In addition， we introduce the facial recovery work using multiview images and video sequences. These low-precision facial recovery methods can gain a wide application space given their flexibility and achieve improved recovery results with the rapid development of deep learning technology. Finally， the future trends in facial realism rendering and recovery methods based on the current state of research our outlined. In the realm of facial realism， existing works often represent the material properties of faces using texture maps and neglect the unique principles of skin coloration as a biological material. Furthermore， the rapid development of deep learning technology increases the importance of exploring of its integration with currently rendering techniques. In terms of inverse recovery， the lack of high-quality open-source datasets often poses limitations on data-based facial recovery methods. In addition， substantial improvement is needed in modeling and recovering details at the skin pore level. Combination of inverse recovery with text-based generative work also holds enormous potential and application scenarios. Hopefully， this paper can provide novice researchers in facial rendering and appearance recovery with valuable background knowledge and inspiration from harmony and ideas.
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