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
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.
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Cited by top-tier papers4
- RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-Consistent DatasetZhongjin Luo, Shengcai Cai, Jinguo Dong, Ruibo Ming et al.CVPR 2023
- SketchFaceGS: Real-Time Sketch-Driven Face Editing and Generation with Gaussian SplattingBo Li, Jiahao Kang, Yubo Ma, Feng-Lin Liu et al.CVPR 2026
- Recovering Dynamic 3D Sketches from VideosJaeah Lee, Changwoon Choi, Young Min Kim, Jaesik ParkCVPR 2025
- REVIVE 3D: Refinement via Encoded Voluminous Inflated prior for Volume EnhancementHankyeol Lee, Wooyeol Baek, Seongdo Kim, Jongyoo KimCVPR 2026
Builds on4
- PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human DigitizationShunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima et al.ICCV 2019 · 1,411 citations
- Learning Manifold Patch-Based Representations of Man-Made ShapesDmitriy Smirnov, Mikhail Bessmeltsev, Justin SolomonICLR 2021 · 5 citations
- Implicit Functions in Feature Space for 3D Shape Reconstruction and CompletionJulian Chibane, Thiemo Alldieck, Gerard Pons-MollCVPR 2020
- PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human DigitizationShunsuke Saito, Tomas Simon, Jason M. Saragih, Hanbyul JooCVPR 2020
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