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

Jungwook Lee, Xuanang Xu, Daeseung Kim, Tianshu Kuang, Hannah H. Deng, Xinrui Song, Yasmine Soubra, Rohan Dharia, Michael A. K. Liebschner, Jaime Gateno, Pingkun Yan

Abstract: 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.

External IDs:dblp:conf/miccai/LeeXKKDSSDLGY25