iOrthoPredictor: Model-guided Deep Prediction of Teeth Alignment

ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2020)

Lingchen Yang, Zefeng Shi, Yiqian Wu, Xiang Li, Kun Zhou, Hongbo Fu, Youyi Zheng*

Abstract: In this paper, we present iOrthoPredictor, a novel system to visually predict teeth alignment in photographs. Our system takes a frontal face image of a patient with visible malpositioned teeth along with a corresponding 3D teeth model as input, and generates a facial image with aligned teeth, simulating a real orthodontic treatment effect. The key enabler of our method is an effective disentanglement of an explicit representation of the teeth geometry from the in-mouth appearance, where the accuracy of teeth geometry transformation is ensured by the 3D teeth model while the in-mouth appearance is modeled as a latent variable. The disentanglement enables us to achieve fine-scale geometry control over the alignment while retaining the original teeth appearance attributes and lighting conditions. The whole pipeline consists of three deep neural networks: a U-Net architecture to explicitly extract the 2D teeth silhouette maps representing the teeth geometry in the input photo, a novel multilayer perceptron (MLP) based network to predict the aligned 3D teeth model, and an encoder-decoder based generative model to synthesize the in-mouth appearance conditional on the original teeth appearance and the aligned teeth geometry. Extensive experimental results and a user study demonstrate that iOrthoPredictor is effective in qualitatively predicting teeth alignment, and applicable to the orthodontic industry.

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author = {Yang, Lingchen and Shi, Zefeng and Wu, Yiqian and Li, Xiang and Zhou, Kun and Fu, Hongbo and Zheng, Youyi},
title = {iOrthoPredictor: model-guided deep prediction of teeth alignment},
year = {2020},
publisher = {Association for Computing Machinery},
volume = {39},
number = {6},
issn = {0730-0301},
journal = {ACM Trans. Graph.},
articleno = {216},
numpages = {15},