从多视图图像通过体绘制重建拓扑一致的人脸网格
Reconstructing Topology-Consistent Face Mesh by Volume Rendering from Multi-View Images
AI总结：
针对工业3D人脸网格重建依赖人工或特定采集的问题，提出结合显式网格与神经体绘制的方法，以距离场为中介、三平面编码辐射场，在保拓扑下优化模板网格，实现更优重建质量。
摘要：
工业级3D人脸资产生成通常需要从多视图图像重建拓扑一致的人脸网格，以用于下游生产流程。但高质量重建往往需要人工处理或特定的采集设置。近年来，NeRF（神经辐射场）通过将场景表示为密度场与辐射场，并利用神经体绘制实现新视图合成，在3D重建领域展现出显著优势。受此启发，我们提出一种新方法，将显式网格与神经体绘制相结合，在保持拓扑不变的前提下，从多视图图像优化艺术家制作的模板人脸网格的几何结构。该方法以距离场为中介，从网格推导密度场，并将辐射场编码在紧凑的三平面（tri-planes）中。为提升收敛性，我们针对网格特性对体绘制进行了多项适配调整。实验表明，与现有方法相比，本方法实现了更优的重建质量，验证了网格与神经体绘制融合的可行性。
英文摘要：
Industrial 3D face assets creation typically reconstructs topology-consistent face meshes from multi-view images for downstream production. However, high-quality reconstruction usually requires manual processing or specific capture settings. Recently NeRF has shown great advantages in 3D reconstruction, by representing scenes as density and radiance fields and utilizing neural volume rendering for novel view synthesis. Inspired by this, we introduce a novel method which combines explicit mesh with neural volume rendering to optimize geometry of an artist-made template face mesh from multi-view images while keeping the topology unchanged. Our method derives density fields from meshes using distance fields as an intermediary and encodes radiance field in compact tri-planes. To improve convergence, several adaptions tailored for meshes are introduced to the volume rendering. Experiments demonstrate that our method achieves superior reconstruction quality compared to previous approaches, validating the feasibility of integrating mesh and neural volume rendering.
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。
