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Sketch-1-to-3: One Single Sketch to 3D Detailed Face Reconstruction by Wen, Liting; Yang, Zimo; Zhang, Xianlin; Ding, Chi; Zhang, Yue; Wang, Mingdao; Li, Xueming is a scholarly article available to read on EtoBox.

What is Sketch-1-to-3: One Single Sketch to 3D Detailed Face Reconstruction about?

3D face reconstruction from a single sketch is a critical yet underexplored task with significant practical applications. The primary challenges stem from the substantial modality gap between 2D sketches and 3D facial structures, including: (1) accurately extracting facial keypoints from 2D sketches; (2) preserving diverse facial expressions and fine-grained texture details; and (3) training a high-performing model with limited data. In this paper, we propose Sketch-1-to-3, a novel framework for realistic 3D face reconstruction from a single sketch, to address these challenges. Specifically, we first introduce the Geometric Contour and Texture Detail (GCTD) module, which enhances the extraction of geometric contours and texture details from facial sketches. Additionally, we design a deep learning architecture with a domain adaptation module and a tailored loss function to align sketches with the 3D facial space, enabling high-fidelity expression and texture reconstruction. To facilitate evaluation and further research, we construct SketchFaces, a real hand-drawn facial sketch dataset, and Syn-SketchFaces, a synthetic facial sketch dataset. Extensive experiments demonstrate that Ske

Author

Wen, Liting; Yang, Zimo; Zhang, Xianlin; Ding, Chi; Zhang, Yue; Wang, Mingdao; Li, Xueming

Published

2025

Language

EN