High-Fidelity Single-Image Head Modeling with Industry-Grade Topology

• URL: http://arxiv.org/abs/2605.04524v1
• Date: Wed, 06 May 2026 06:07:35 GMT
• Title: High-Fidelity Single-Image Head Modeling with Industry-Grade Topology
• Abstract summary: Our framework adopts a coarse-to-fine optimization pipeline that refines a rigged template across three stages.<n>To mitigate the ill-posed nature of single-image 3D face reconstruction, we employ a normal consistency objective jointly with landmark alignment.<n>Our hierarchical optimization with geometry-aware regularization yields meshes with semantically meaningful edge flow and industry-grade topology.
• Score: 7.585051164429312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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