SimpModeling: Sketching Implicit Field to Guide Mesh Modeling for 3D Animalmorphic Head Design

Zhongjin Luo, Jie Zhou, Heming Zhu, Dong Du, Xiaoguang Han*, Hongbo Fu*

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

26 Citations (Scopus)
56 Downloads (CityUHK Scholars)

Abstract

Head shapes play an important role in 3D character design. In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily model 3D animalmorphic heads - a prevalent kind of heads in character design. Although sketching provides an easy way to depict desired shapes, it is challenging to infer dense geometric information from sparse line drawings. Recently, deepnet-based approaches have been taken to address this challenge and try to produce rich geometric details from very few strokes. However, while such methods reduce users' workload, they would cause less controllability of target shapes. This is mainly due to the uncertainty of the neural prediction. Our system tackles this issue and provides good controllability from three aspects: 1) we separate coarse shape design and geometric detail specification into two stages and respectively provide different sketching means; 2) in coarse shape designing, sketches are used for both shape inference and geometric constraints to determine global geometry, and in geometric detail crafting, sketches are used for carving surface details; 3) in both stages, we use the advanced implicit-based shape inference methods, which have strong ability to handle the domain gap between freehand sketches and synthetic ones used for training. Experimental results confirm the effectiveness of our method and the usability of our interactive system. We also contribute to a dataset of high-quality 3D animal heads, which are manually created by artists.
Original languageEnglish
Title of host publicationUIST'21 - Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages854-863
ISBN (Print)9781450386357
DOIs
Publication statusPublished - 2021
Event34th Annual ACM Symposium on User Interface Software and Technology (UIST 2021) - Virtual, United States
Duration: 10 Oct 202114 Oct 2021

Publication series

NameUIST - Proceedings of the Annual ACM Symposium on User Interface Software and Technology

Conference

Conference34th Annual ACM Symposium on User Interface Software and Technology (UIST 2021)
Abbreviated titleUIST’21
PlaceUnited States
Period10/10/2114/10/21

Research Keywords

  • 3D modeling interface
  • datasets
  • implicit fields
  • neural networks

Publisher's Copyright Statement

  • COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: © Owner/author | ACM 2021. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology, https://doi.org/10.1145/3472749.3474791.

RGC Funding Information

  • RGC-funded

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