3D shapes from scanning, reconstruction, or AI-generated content often lack simple quad mesh layouts—critical for efficient editing and modeling. Existing quad-remeshing techniques typically produce complex layouts with irregular loops, leading to tedious manual cleanup and extensive algorithm tuning.
We introduce SQuadGen, a diffusion-based generative framework that leverages Chart Distance Fields (CDF) to synthesize simple quad layouts on 3D shapes. Our approach addresses two key challenges: (1) the discrete nature of mesh connectivity, which hinders learning, and (2) the scarcity of large-scale datasets with simple quad meshes.
To overcome the first, we propose CDF, a continuous surface-based representation enabling effective learning and synthesis of quad layouts. To address the second, we define loop-aware simplicity metrics and construct a large-scale dataset of high-quality quad layouts recovered from public 3D repositories through a robust quad-recovery pipeline.
Extensive evaluations across diverse 3D inputs show that SQuadGen consistently outperforms existing methods, producing robust, artist-friendly simple quad layouts.
Coming soon.
Coming soon.
@article{kong2026squadgen,
title = {SQuadGen: Generating Simple Quad Layouts via Chart Distance Fields},
author = {Kong, Youkang and Liu, Yang and Dong, Yue and Tong, Xin and Shum, Heung-Yeung},
journal = {ACM Trans. Graph. (SIGGRAPH)},
volume = {45},
number = {4},
year = {2026},
}