SQuadGen is a diffusion-based generative framework for synthesizing simple quad layouts on 3D shapes. It introduces Chart Distance Fields (CDF), a continuous surface-based representation that makes quad-layout generation amenable to neural learning while preserving structure useful for downstream modeling and editing.
This repository hosts the pretrained checkpoints and matching configuration files used by the official inference scripts. It is intended as the model release page; installation, data preparation, training, evaluation, and full inference instructions are maintained in the GitHub repository.
The released files are native PyTorch checkpoints. The sqvae_geomae.pth checkpoint contains both the SQ-VAE and Geometry-AE components; SQ-Diffuse inference requires both this checkpoint and sqdiffuse.pth.
All inference, training and evaluation scripts live in the official GitHub repository. After cloning and setting up the environment as described there, point the --ae_pth / --model_pth arguments at the corresponding files downloaded from this repo, e.g.:
After installing the environment described in the GitHub repository, pass ./ckpts_hf/ckpts/sqvae_geomae.pth and ./ckpts_hf/ckpts/sqdiffuse.pth to the corresponding inference scripts.
The models were trained on publicly available 3D geometry datasets, including Objaverse, Amazon Berkeley Objects (ABO), and ShapeNet. Dataset-specific terms, attribution requirements, and permitted uses remain applicable to users who work with those datasets or derived assets.
Performance was evaluated on academic quad-meshing and reconstruction benchmarks described in the paper. Robustness coverage is limited to the tested datasets; the release does not claim a unified standardized comparison against all third-party models.
SQuadGen is a research prototype for 3D geometry encoding and quad-layout generation. It is not intended for safety-critical, high-risk, real-time, or fully autonomous use, including production CAD, engineering, simulation, or manufacturing workflows, unless additional validation and safeguards are applied.
The models are trained on publicly available 3D datasets, including Objaverse, ABO, and ShapeNet. Their performance may therefore reflect gaps in dataset coverage: results are generally stronger for common object categories and clean meshes, and may degrade for rare or highly complex shapes, noisy or non-manifold meshes, irregular topology, strict CAD constraints, or other out-of-distribution inputs. The models do not provide guarantees of geometric correctness, mesh validity, manufacturability, robustness, or consistency across datasets and mesh resolutions.
Generated layouts and intermediate representations should be treated as learned approximations. Before downstream use, developers should validate input geometry and output quality, use fallback heuristics where appropriate, and retain human review for decisions that affect design, simulation, engineering, or manufacturing. Users should also verify that input geometry is legally and appropriately sourced. These models do not process language or make decisions in sensitive domains such as healthcare or legal services.
@article{kong2026squadgen,
author = {Youkang Kong and Yang Liu and Yue Dong and Xin Tong and Heung-Yeung Shum},
title = {{SQuadGen}: Generating Simple Quad Layouts via Chart Distance Fields},
journal = {ACM Transactions on Graphics (SIGGRAPH)},
volume = {45},
number = {4},
pages = {144:1--144:15},
year = {2026},
doi = {10.1145/3811348}
}
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