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

TL;DR

This paper introduces a single-image head reconstruction method that produces high-quality, industry-grade topology meshes by combining a coarse-to-fine optimization with geometry-aware regularizations.

Contribution

It proposes a novel hierarchical optimization framework with geometry-aware constraints to achieve stable, topologically regular head meshes from a single image.

Findings

Achieved stable convergence and consistent topology in head meshes.

Participants ranked the method as top-performing in a user study.

Produced meshes with semantically meaningful edge flow and high visual quality.

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.