Envision: Geometry-Aware Setwise Ranking for Facial Surgery Visualization

Stochastic image editing can generate several plausible facial surgery visualizations, but selecting one without postoperative ground truth is difficult. Identity preservation and visual realism alone do not establish whether an edit captures the intended anatomical change. We introduce Envision, a geometry-aware setwise ranker for rhinoplasty, blepharoplasty, and rhytidectomy. A fixed FLUX.1-Fill backbone generates candidate pools using different random seeds and edit intensities. The ranker combines procedure-normalized morphometric changes, geometric information pooled across candidates, and learned appearance features. A procedure-conditioned direction predictor assesses how each candidate's change relates to the estimated case-level direction. Paired postoperative images provide teacher supervision during training, whereas inference uses only the preoperative portrait, procedure label, and generated candidates. Across a 125-case evaluation cohort combining validation and test, Envision improves mean teacher SurgicalScore over a handcrafted selector by 0.034 with 15 candidates and 0.038 with 25 candidates, recovering approximately 19% and 18% of the available oracle headroom, respectively. The gains chiefly reflect better agreement in the direction and magnitude of geometric change and are strongest for rhytidectomy, although cached-vision ablations do not isolate a benefit from the setwise or geometric branches alone. Performance levels off beyond 15 candidates, and the test-only improvements remain statistically inconclusive. These results suggest that geometry-aware selection can improve surgical visualizations without changing the generator. They do not establish that the selected images predict individual clinical outcomes.