Face meshes in consistent topology serve as the foundation for many face-related applications, such as 3DMM constrained face reconstruction and expression retargeting. Traditional methods commonly acquire topology uniformed face meshes by two separate steps: multi-view stereo (MVS) to reconstruct shapes followed by non-rigid registration to align topology, but struggles with handling noise and non-lambertian surfaces. Recently neural volume rendering techniques have been rapidly evolved and shown great advantages in 3D reconstruction or novel view synthesis. Our goal is to leverage the superiority of neural volume rendering into multi-view reconstruction of face mesh with consistent topology. We propose a mesh volume rendering method that enables directly optimizing mesh geometry while preserving topology, and learning implicit features to model complex facial appearance from multi-view images. The key innovation lies in spreading sparse mesh features into the surrounding space to simulate radiance field required for volume rendering, which facilitates backpropagation of gradients from images to mesh geometry and implicit appearance features. Our proposed feature spreading module exhibits deformation invariance, enabling photorealistic rendering seamlessly after mesh editing. We conduct experiments on multi-view face image dataset to evaluate the reconstruction and implement an application for photorealistic rendering of animated face mesh.

翻译：具有一致拓扑结构的人脸网格是许多面部相关应用（如3DMM约束的面部重建和表情重定向）的基础。传统方法通常通过两个独立步骤获取拓扑一致的人脸网格：先通过多视图立体（MVS）重建形状，再通过非刚性配准对齐拓扑，但在处理噪声和非朗伯表面时效果不佳。近年来，神经体渲染技术快速发展，在三维重建或新视角合成方面展现出巨大优势。我们的目标是将神经体渲染的优势引入到具有一致拓扑的人脸网格多视图重建中。我们提出一种网格体渲染方法，能够在保持拓扑结构的同时直接优化网格几何形状，并从多视图图像中学习隐式特征以建模复杂面部外观。关键创新在于将稀疏网格特征扩散到周围空间，以模拟体渲染所需的辐射场，从而促进图像到网格几何形状和隐式外观特征的梯度反向传播。我们提出的特征扩散模块具有变形不变性，使网格编辑后能够无缝实现照片级真实感渲染。我们在多视图人脸图像数据集上进行重建评估，并实现了一个用于动态人脸网格照片级真实感渲染的应用。