We present a single-image head mesh reconstruction framework that addresses the longstanding challenge of simultaneously preserving facial identity and producing industry-grade topology. Our framework adopts a coarse-to-fine optimization pipeline that refines a rigged template across three stages -- rig, joint, and vertex -- achieving stable convergence and consistent topology. To mitigate the ill-posed nature of single-image 3D face reconstruction and ensure identity preservation, we employ a normal consistency objective jointly with landmark alignment. To further preserve local surface structure and enforce topological regularity, we introduce geometry-aware constraints based on Gaussian curvature and conformal consistency, along with auxiliary regularizations that correct fine artifacts such as lip seams and eyelid discontinuities. Our hierarchical optimization with geometry-aware regularization yields meshes with semantically meaningful edge flow and industry-grade topology. After geometry reconstruction, we extract UV-space texture and normal maps to preserve appearance details for visualization and downstream use. In a user study with 22 professional technical artists, our results were assessed as approaching industry-grade usability, and 95% of participants ranked our method as the top-performing approach, underscoring its effectiveness for real-world digital human production.

翻译：我们提出了一种基于单张图像的人头网格重建框架，旨在解决同时保持面部身份特征与生成工业级拓扑这一长期难题。该框架采用从粗到细的优化管线，通过三个阶段——绑定、关节与顶点——对带骨骼模板进行迭代优化，实现稳定收敛与拓扑一致性。为缓解单图三维人脸重建的病态问题并确保身份保真，我们联合使用法线一致性目标与关键点对齐策略。为进一步保持局部表面结构并施加拓扑规则性，我们引入了基于高斯曲率与共形一致性的几何感知约束，以及辅助正则化项以修正唇缝、眼睑不连续等细微伪影。这种结合几何感知正则化的分层优化方法，能够生成具有语义化边流与工业级拓扑的网格。几何重建完成后，我们提取UV空间纹理与法线贴图，以保留外观细节供可视化与下游应用。在由22名专业美术师参与的用户研究中，我们的结果被评估为接近工业级可用性，且95%的参与者将本方法评为最优方案，充分验证了其在真实数字人制作中的有效性。