Facial Appearance Prediction with Conditional Multi-scale Autoregressive Modeling for Orthognathic Surgical Planning

Craniomaxillofacial deformities often necessitate orthognathic surgery to correct jaw positions and improve both function and aesthetics. The existing patient-specific optimal face prediction for soft-tissue-driven planning struggles to accurately capture fine facial details and maintain harmonious alignment among key facial features. In this paper, we propose a novel Conditional Autoregressive Modeling for Orthognathic Surgery (CAMOS) framework that directly predicts patients’ optimal 3D face from their preoperative appearance. Our approach employs a hierarchical, coarse-to-fine next-scale prediction strategy, beginning with large-scale pretraining on 44,602 control faces to construct a robust generative model that captures diverse demographic features. Subsequently, the model is fine-tuned on an in-house dataset of 86 orthognathic surgery patients, establishing a conditional path that integrates patient-specific information to form a conditional generative model. Evaluation on both public and in-house datasets demonstrates that CAMOS successfully generates patient-specific optimal face with high quality, effectively addressing the limitations of prior single-step approaches. Source code is available at https://github.com/RPIDIAL/CAMOS.