We present SplatFace, a novel Gaussian splatting framework designed for 3D
human face reconstruction without reliance on accurate pre-determined geometry.
Our method is designed to simultaneously deliver both high-quality novel view
rendering and accurate 3D mesh reconstructions. We incorporate a generic 3D
Morphable Model (3DMM) to provide a surface geometric structure, making it
possible to reconstruct faces with a limited set of input images. We introduce
a joint optimization strategy that refines both the Gaussians and the morphable
surface through a synergistic non-rigid alignment process. A novel distance
metric, splat-to-surface, is proposed to improve alignment by considering both
the Gaussian position and covariance. The surface information is also utilized
to incorporate a world-space densification process, resulting in superior
reconstruction quality. Our experimental analysis demonstrates that the
proposed method is competitive with both other Gaussian splatting techniques in
novel view synthesis and other 3D reconstruction methods in producing 3D face
meshes with high geometric precision.

本文提出了一种新颖的高斯状喷洒框架 SplatFace，它用于无需准确的预定义几何形状进行 3D 人脸重建。该方法同时提供高质量的新视角渲染和精确的 3D 网格重建，通过引入通用的 3D 可塑模型（3DMM）来提供表面几何结构，从而使得以有限的图片集合进行人脸重建成为可能。作者提出了一种联合优化策略，通过协同非刚性对准过程来改善高斯函数和可塑性表面。还提出了一种新的距离度量方法，即喷洒 - 表面距离，通过考虑高斯位置和协方差来改善对准。利用表面信息还用于引入世界空间的稠密化过程，从而获得更好的重建质量。实验分析表明，该方法在新视角合成的高斯状喷洒技术和 3D 重建方法中均具有竞争力，能够生成具有高几何精度的 3D 人脸网格。