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
this https URL

研究通过学习基于皱纹线距离场的面部细节结构表示，提出了一种结构感知可编辑形变模型（SEMM），运用 SEMM 可进行面部细节编辑并保持面部身份信息，包括表情合成权重的转换、年龄值的编辑等。实验结果表明，该模型表现出色，优于现有的方法。