TeethGenerator: A two-stage framework for paired pre- and post-orthodontic 3D dental data generation
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Digital orthodontics represents a prominent and critical application of
computer vision technology in the medical field. So far, the labor-intensive
process of collecting clinical data, particularly in acquiring paired 3D
orthodontic teeth models, constitutes a crucial bottleneck for developing tooth
arrangement neural networks. Although numerous general 3D shape generation
methods have been proposed, most of them focus on single-object generation and
are insufficient for generating anatomically structured teeth models, each
comprising 24-32 segmented teeth. In this paper, we propose TeethGenerator, a
novel two-stage framework designed to synthesize paired 3D teeth models pre-
and post-orthodontic, aiming to facilitate the training of downstream tooth
arrangement networks. Specifically, our approach consists of two key modules:
(1) a teeth shape generation module that leverages a diffusion model to learn
the distribution of morphological characteristics of teeth, enabling the
generation of diverse post-orthodontic teeth models; and (2) a teeth style
generation module that synthesizes corresponding pre-orthodontic teeth models
by incorporating desired styles as conditional inputs. Extensive qualitative
and quantitative experiments demonstrate that our synthetic dataset aligns
closely with the distribution of real orthodontic data, and promotes tooth
alignment performance significantly when combined with real data for training.
The code and dataset are available at
https://github.com/lcshhh/teeth_generator.
