Parallax Portrait Matting

Image matting is highly ill-posed, especially when both theforeground and background are richly textured. While single-image mat-ting methods learn strong priors from data, they often struggle on thesechallenging cases. Existing approaches improve results by requiring ad-ditional signals such as green screens, polarized lighting, or clean back-ground images, but these typically rely on specialized capture setups.We present Parallax Portrait Matting, a practical two-frame mattingmethod that uses a second image captured with slight viewpoint change.Such a setting arises naturally in burst photography, where small cameramotion induces foreground-background parallax and provides comple-mentary observations for matting. Our pipeline estimates trimaps andforeground/background motion, then constructs aligned views for pre-diction. To handle imperfect motion estimation, the network uses thebackground-aligned pair for direct fusion and the foreground-aligned cuethrough cross-attention for error compensation. Experiments show thatour method recovers finer details and more accurate foreground colorsthan strong single-image matting baselines on challenging portrait cases.