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Patients take care of what their teeth will be like after the orthodontics.Orthodontists usually describe the expectation movement based on the originalsmile images, which is unconvincing. The growth of deep-learning generativemodels change this situation. It can visualize the outcome of orthodontictreatment and help patients foresee their future teeth and facial appearance.While previous studies mainly focus on 2D or 3D virtual treatment outcome (VTO)at a profile level, the problem of simulating treatment outcome at a frontalfacial image is poorly explored. In this paper, we build an efficient andaccurate system for simulating virtual teeth alignment effects in a frontalfacial image. Our system takes a frontal face image of a patient with visiblemalpositioned teeth and the patient's 3D scanned teeth model as input, andprogressively generates the visual results of the patient's teeth given thespecific orthodontics planning steps from the doctor (i.e., the specificationof translations and rotations of individual tooth). We design a multi-modalencoder-decoder based generative model to synthesize identity-preservingfrontal facial images with aligned teeth. In addition, the original image colorinformation is used to optimize the orthodontic outcomes, making the resultsmore natural. We conduct extensive qualitative and clinical experiments andalso a pilot study to validate our method.

OrthoGAN:High-Precision Image Generation for Teeth Orthodontic Visualization