Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details. This paper augments morphable models in representing facial details by learning a Structure-aware Editable Morphable Model (SEMM). SEMM introduces a detail structure representation based on the distance field of wrinkle lines, jointly modeled with detail displacements to establish better correspondences and enable intuitive manipulation of wrinkle structure. Besides, SEMM introduces two transformation modules to translate expression blendshape weights and age values into changes in latent space, allowing effective semantic detail editing while maintaining identity. Extensive experiments demonstrate that the proposed model compactly represents facial details, outperforms previous methods in expression animation qualitatively and quantitatively, and achieves effective age editing and wrinkle line editing of facial details. Code and model are available at https://github.com/gerwang/facial-detail-manipulation.
翻译:3D 面部的统计模型必须采用可变模型。 以往关于可变模型的工作主要侧重于大型面部几何学,但忽略面部细节。 本文通过学习结构上可编辑软体模型(SEMM),强化了显示面部细节的可变模型。 SEMM 引入了基于皱纹线条距离范围的详细结构表示, 与详细模型共同建模, 以建立更好的对应关系, 并能够直观地操纵皱纹结构。 此外, SEMM 引入了两个转换模块, 将表达式混合形状重量和年龄值转换为潜在空间的变化, 允许在维护身份的同时进行有效的语义细节编辑。 广泛的实验表明, 拟议的模型代表面部细节, 超越了先前在动画质量和数量上所用的方法, 实现了有效的年龄编辑和面部细节的皱纹线编辑。 代码和模型可在 https://github.com/gerwang/ facial- detail-manipulation上查阅。