StereoEdit: A Diffusion-Based Framework for Stereo-Consistent Image Editing

While recent diffusion models have achieved remarkable suc-cess in visual content editing, extending them to stereo image editingremains highly challenging due to the lack of explicit stereo geomet-ric consistency alignment, which leads to cross-view inconsistencies andgeometric distortions. We propose StereoEdit, a training-free diffusionframework designed to achieve geometrically consistent and structurallycoherent stereo editing. StereoEdit introduces two complementary mod-ules that explicitly enforce geometric reliability during the denoising pro-cess. The Geometrically-Gated Attention Reference (GGAR) adaptivelyfilters cross-view attention based on disparity-aware confidence, ensur-ing stable correspondences across epipolar regions, while the Geometry-Aware Latent Alignment (GALA) performs confidence-weighted, bidi-rectional latent corrections to maintain parallax alignment throughoutthe diffusion process. Together, these modules enable robust and consis-tent stereo edits. Extensive experiments on diverse stereo benchmarksdemonstrate that StereoEdit significantly improves cross-view coherenceand visual realism, establishing a new foundation for controllable andimmersive stereoscopic content editing.