Modeling 3D avatars benefits various application scenarios such as AR/VR,
gaming, and filming. Character faces contribute significant diversity and
vividity as a vital component of avatars. However, building 3D character face
models usually requires a heavy workload with commercial tools, even for
experienced artists. Various existing sketch-based tools fail to support
amateurs in modeling diverse facial shapes and rich geometric details. In this
paper, we present SketchMetaFace - a sketching system targeting amateur users
to model high-fidelity 3D faces in minutes. We carefully design both the user
interface and the underlying algorithm. First, curvature-aware strokes are
adopted to better support the controllability of carving facial details.
Second, considering the key problem of mapping a 2D sketch map to a 3D model,
we develop a novel learning-based method termed "Implicit and Depth Guided Mesh
Modeling" (IDGMM). It fuses the advantages of mesh, implicit, and depth
representations to achieve high-quality results with high efficiency. In
addition, to further support usability, we present a coarse-to-fine 2D
sketching interface design and a data-driven stroke suggestion tool. User
studies demonstrate the superiority of our system over existing modeling tools
in terms of the ease to use and visual quality of results. Experimental
analyses also show that IDGMM reaches a better trade-off between accuracy and
efficiency. SketchMetaFace are available at
this https URL

本文介绍了 SketchMetaFace 系统，它为业余用户提供了一个快速建立逼真立体脸部的草图系统，使用了曲率感知笔画以及一种名为 “显式深度引导网格建模” 的新学习方法，通过数据驱动的笔画建议工具和分阶段标注接口设计增强了系统的可用性，并通过用户研究和实验分析验证其优越性。