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.
我们提出了SplatFace，这是一个新颖的高斯喷溅框架，专为3D人脸重建设计，而不依赖于精确预定的几何形状。我们的方法旨在同时提供高质量的新视角渲染和精确的3D网格重建。我们结合了一个通用的3D可变形模型（3DMM）来提供表面几何结构，使得仅使用有限的输入图像就能重建面孔成为可能。我们引入了一种联合优化策略，通过一种协同的非刚性对齐过程，细化高斯和可变形表面。提出了一种新的距离度量方法，即喷溅到表面，通过考虑高斯位置和协方差来改善对齐。表面信息也被用来结合一个世界空间密集化过程，从而实现更优的重建质量。我们的实验分析表明，所提出的方法在新视角合成方面与其他高斯喷溅技术竞争，在生成高几何精度的3D面部网格方面与其他3D重建方法竞争。