Envisage: Diffusion-Based Rhinoplasty Goal Visualization with Mask-Decomposed Evaluation
Localized generative editing needs localized evaluation: full-image identity metrics are structurally confounded under hard-composited edits. We present Envisage, a FLUX.1-Fill inpainting reference pipeline for rhinoplasty goal visualization from a single frontal photograph. The pipeline combines 8 rhinoplasty clinical presets (the released framework also includes 8 blepharoplasty and 8 rhytidectomy presets), MediaPipe masks, and hard-mask compositing. The composite preserves outside-mask pixels by construction, so full-face identity scores are dominated by copied pixels rather than by the diffusion backbone. Because full-face identity metrics cannot grade localized edits, we introduce Su...
| Authors | Mudit Agarwal, Amit D. Bhrany |
|---|---|
| Primary category | eess.IV |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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