Pro-Pose: Unpaired Full-Body Portrait Synthesis via Canonical UV Maps

Photographs of people taken by professional photographerstypically present the person in beautiful lighting, with an interestingpose, and flattering quality. This is unlike common photos people takeof themselves in uncontrolled conditions. In this paper, we explore howto canonicalize a person’s "in-the-wild" photograph into a controllable,high-fidelity avatar—reposed in a simple environment with standard-ized minimal clothing. A key challenge is preserving the person’s uniquewhole-body identity, facial features, and body shape while stripping awaythe complex occlusions of their original garments. While a large paireddataset of the same person in varied clothing and poses would simplifythis, such data does not exist. To that end, we propose two key insights:1) Our method transforms the input photo into a canonical full-body UVspace, which we couple with a novel reposing methodology to model oc-clusions and synthesize novel views. Operating in UV space allows us todecouple pose from appearance and leverage massive unpaired datasets.2) We personalize the output photo via multi-image finetuning to ensurerobust identity preservation under extreme pose changes. Our approachyields high-quality, reposed portraits that achieve strong quantitativeperformance on real-world imagery, providing an ideal, clean biometriccanvas that significantly improves the fidelity of downstream applicationslike Virtual Try-On (VTO).