SplatFace：利用可优化表面的高斯泼溅人脸重建
SplatFace: Gaussian Splat Face Reconstruction Leveraging an Optimizable Surface
AI总结：
提出SplatFace，一种结合3DMM表面与高斯泼溅的联合优化框架，通过splat-to-surface距离度量实现无需精确几何的高质量三维人脸重建与新视角渲染。
摘要：
我们提出了SplatFace，一种新颖的高斯泼溅框架，旨在无需依赖精确预定义几何的情况下进行三维人脸重建。我们的方法旨在同时提供高质量的新视角渲染和精确的三维网格重建。我们整合了一个通用的三维可变形模型（3DMM）来提供表面几何结构，使得仅用有限数量的输入图像就能重建人脸。我们引入了一种联合优化策略，通过协同的非刚性对齐过程来细化高斯和可变形表面。提出了一种新的距离度量，即泼溅到表面（splat-to-surface），通过考虑高斯位置和协方差来改善对齐。表面信息还被用于整合世界空间中的致密化过程，从而带来更优的重建质量。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泼溅技术相比具有竞争力，在生成具有高几何精度的三维人脸网格方面也与其他三维重建方法相当。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
