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.

翻译：我们提出了SplatFace，一种新颖的高斯泼溅框架，专为无需依赖精确预定义几何的三维人脸重建而设计。该方法旨在同时实现高质量的新视角渲染与精准的三维网格重建。我们引入通用三维可变形模型（3DMM）来提供曲面几何结构，从而使得在有限输入图像集的情况下也能完成人脸重建。我们提出了一种联合优化策略，通过协同的非刚性对齐过程同时优化高斯体与可变形曲面。我们设计了名为“泼溅到曲面”（splat-to-surface）的新型距离度量，通过综合考虑高斯体位置与协方差来改善对齐效果。同时利用曲面信息引入世界空间稠密化过程，显著提升重建质量。实验分析表明，所提方法在新视角合成方面与其他高斯泼溅技术具有竞争力，在生成高几何精度三维人脸网格方面亦能与其它三维重建方法相抗衡。