TY - JOUR
T1 - SketchMetaFace
T2 - A Learning-Based Sketching Interface for High-Fidelity 3D Character Face Modeling
AU - Luo, Zhongjin
AU - Du, Dong
AU - Zhu, Heming
AU - Yu, Yizhou
AU - Fu, Hongbo
AU - Han, Xiaoguang
N1 - Publisher Copyright:
© 1995-2012 IEEE.
PY - 2024/8
Y1 - 2024/8
N2 - 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.
AB - 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.
KW - Face modeling
KW - neural network
KW - sketch-based 3D modeling
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001262914400075
UR - https://openalex.org/W4384819398
UR - https://www.scopus.com/pages/publications/85165264177
U2 - 10.1109/TVCG.2023.3291703
DO - 10.1109/TVCG.2023.3291703
M3 - Journal Article
C2 - 37467083
SN - 1077-2626
VL - 30
SP - 5260
EP - 5275
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 8
ER -