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

arXiv: https://arxiv.org/abs/2108.02548 · HTML: https://mlsanity.com/paper/2108.02548/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion](https://mlsanity.com/paper/2003.01456/) (2020)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [MarrNet: 3D Shape Reconstruction via 2.5D Sketches](https://mlsanity.com/paper/1711.03129/) (2017)
- [3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks](https://mlsanity.com/paper/1707.06375/) (2017)
- [3D Sketching using Multi-View Deep Volumetric Prediction](https://mlsanity.com/paper/1707.08390/) (2017)
- [P-Cloth: Interactive Complex Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integrators](https://mlsanity.com/paper/2008.00409/) (2020)
- [DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling](https://mlsanity.com/paper/1706.02042/) (2017)
- [Sketch2CAD: Sequential CAD Modeling by Sketching in Context](https://mlsanity.com/paper/2009.04927/) (2020)
- [Learning Manifold Patch-Based Representations of Man-Made Shapes](https://mlsanity.com/paper/1906.12337/) (2019)

## Built on by (most cited first, 9 of 9)

- [RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset](https://mlsanity.com/paper/2303.12564/) (2023)
- [GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single In-the-Wild Image using a Dataset with Levels of Details](https://mlsanity.com/paper/2411.03047/) (2024)
- [SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling](https://mlsanity.com/paper/2307.00804/) (2023)
- [Deep Sketch-Based 3D Modeling: A Survey](https://mlsanity.com/paper/2603.03287/) (2026)
- [AniArtAvatar: Animatable 3D Art Avatar from a Single Image](https://mlsanity.com/paper/2403.17631/) (2024)
- [Recovering Dynamic 3D Sketches from Videos](https://mlsanity.com/paper/2503.20321/) (2025)
- [SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing](https://mlsanity.com/paper/2212.04065/) (2022)
- [SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D Human Reconstruction](https://mlsanity.com/paper/2310.06577/) (2023)
- [SketchFaceGS: Real-Time Sketch-Driven Face Editing and Generation with Gaussian Splatting](https://mlsanity.com/paper/2604.19202/) (2026)

## Abstract (arXiv)

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.