SketchMetaFace: A Learning-Based Sketching Interface for High-Fidelity 3D Character Face Modeling

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 e...

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Vydané v:IEEE transactions on visualization and computer graphics Ročník 30; číslo 8; s. 5260 - 5275
Hlavní autori: Luo, Zhongjin, Du, Dong, Zhu, Heming, Yu, Yizhou, Fu, Hongbo, Han, Xiaoguang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Artists
Avatars
Computational modeling
Face modeling
Faces
Image reconstruction
Learning
Load modeling
Modelling
neural network
Shape
sketch-based 3D modeling
Solid modeling
Three dimensional models
Three-dimensional displays
ISSN:1077-2626, 1941-0506, 1941-0506
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Shrnutí: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 article, 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.
Bibliografia:ObjectType-Article-1
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2023.3291703