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
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
https://zhongjinluo.github.io/SketchMetaFace/.
64a3a49f50091725077b800f
Zhongjin Luo
claimed_verified
2023-07-05T18:56:01.487Z
63a31e22a0ed04b823c01015
/avatars/455f2de6dab9504c154dad708a1fb74d.svg
Zhongjin
user
xiaojin66
64a3a49f50091725077b8010
Dong Du
64a3a49f50091725077b8011
Heming Zhu
claimed_verified
2023-07-04T19:16:34.483Z
64a41b3da3f7783f6c13bced
https://cdn-avatars.huggingface.co/v1/production/uploads/64a41b3da3f7783f6c13bced/M-w8PUAs64hG2IRguZuf7.jpeg
Heming  Zhu
user
kv2000
64a3a49f50091725077b8012
Yizhou Yu
64a3a49f50091725077b8013
Hongbo Fu
64a3a49f50091725077b8014
Xiaoguang Han
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
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
https://zhongjinluo.github.io/SketchMetaFace/.
A sketch-based system called SketchMetaFace enables amateur users to model high-fidelity 3D faces efficiently by using curvature-aware strokes and a novel method called Implicit and Depth Guided Mesh Modeling (IDGMM).
curvature-aware strokes Implicit and Depth Guided Mesh Modeling IDGMM mesh representations implicit representations depth representations coarse-to-fine data-driven stroke suggestions