mlsanityData to 14 Oct 2026

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

arXiv 2108.02548 ↗First posted 5 Aug 2021cs.CV cs.HCcited by 9

Not on the map. Why?

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.

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

15 of 46 references.

References · 15

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  3. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
  4. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  5. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
  6. 2020PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitizationcited by 521
  7. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  8. 20163D Menagerie: Modeling the 3D shape and pose of animalscited by 312
  9. 2017MarrNet: 3D Shape Reconstruction via 2.5D Sketchescited by 108
  10. 20173D Shape Reconstruction from Sketches via Multi-view Convolutional Networkscited by 45
  11. 20173D Sketching using Multi-View Deep Volumetric Predictioncited by 37
  12. 2020P-Cloth: Interactive Complex Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integratorscited by 26
3 more
  1. 2017DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modelingcited by 16
  2. 2020Sketch2CAD: Sequential CAD Modeling by Sketching in Contextcited by 14
  3. 2019Learning Manifold Patch-Based Representations of Man-Made Shapescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1; 2026: 2.

9 papers.