Facial appearance prediction for orthognathic surgery with diffusion models

Orthognathic surgery corrects craniomaxillofacial deformities by repositioning skeletal structures to improve facial aesthetics and function. Conventional orthognathic surgical planning is largely bone-driven, where bone repositioning is first defined and soft-tissue outcomes are predicted. However, this is limited by its reliance on surgeon-defined bone plans and the inability to directly optimize for patient-specific aesthetic outcomes. To address these limitations, the soft-tissue-driven paradigm seeks to first predict a patient-specific optimal facial appearance and subsequently derive the skeletal changes required to achieve it. In this work, we introduce FAPOS (Facial Appearance Prediction for Orthognathic Surgery), a novel transformer-based latent diffusion framework that directly predicts a normal-looking 3D facial outcome from pre-operative scans to allow soft-tissue driven planning. FAPOS utilizes a dense 282-landmark representation and is trained on a combined dataset of 44,602 public 3D faces, overcoming limitations of data scarcity, lack of correspondence. Our three-phase training pipeline combines geometric encoding, latent diffusion modeling, and patient-specific conditioning. Quantitative and qualitative results show that FAPOS outperforms prior methods with improved facial symmetry and identity preservation. These results mark an important step toward enabling soft-tissue-driven surgical planning, with FAPOS providing an optimal facial target that serves as the basis for estimating the skeletal adjustments in subsequent stages.