Digital crown design remains a labor-intensive bottleneck in restorative dentistry. We present CrownGen, a generative framework that automates patient-customized crown design using a denoising diffusion model on a novel tooth-level point cloud representation. The system employs two core components: a boundary prediction module to establish spatial priors and a diffusion-based generative module to synthesize high-fidelity morphology for multiple teeth in a single inference pass. We validated CrownGen through a quantitative benchmark on 496 external scans and a clinical study of 26 restoration cases. Results demonstrate that CrownGen surpasses state-of-the-art models in geometric fidelity and significantly reduces active design time. Clinical assessments by trained dentists confirmed that CrownGen-assisted crowns are statistically non-inferior in quality to those produced by expert technicians using manual workflows. By automating complex prosthetic modeling, CrownGen offers a scalable solution to lower costs, shorten turnaround times, and enhance patient access to high-quality dental care.
Cite
@article{arxiv.2512.21890,
title = {CrownGen: Patient-customized Crown Generation via Point Diffusion Model},
author = {Juyoung Bae and Moo Hyun Son and Jiale Peng and Wanting Qu and Wener Chen and Zelin Qiu and Kaixin Li and Xiaojuan Chen and Yifan Lin and Hao Chen},
journal= {arXiv preprint arXiv:2512.21890},
year = {2026}
}