Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely on geometric face models that cannot represent fine-grained details in texture with a mesh discretization and linear deformation designed to model only a coarse face geometry. We introduce a method that bridges this gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. This blending is performed by measuring local volumetric changes in those expressions and locally reproducing their appearance whenever a similar expression is performed at test time. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces.

翻译：生成忠实的人脸可视化需要捕捉面部几何与外观的粗粒度与细粒度细节。现有方法要么依赖数据驱动，需要研究社区无法公开获取的大规模数据语料库；要么因依赖几何面部模型而无法捕捉精细细节——此类模型采用仅适用于粗糙面部几何建模的网格离散化与线性形变，难以表示纹理中的细粒度细节。我们提出一种方法弥合这一差距，其灵感源自传统计算机图形学技术。通过混合稀疏极端姿态集合的外观，对新表情进行建模。该混合过程通过测量这些表情中的局部体积变化，并在测试时当类似表情出现时局部复现其外观来实现。实验表明，我们的方法可泛化至未见过的表情，在面部平滑体积形变之上添加细粒度效果，并展示了其超越面部的泛化能力。