PanoErase:Spherical Geometry-Aware Panoramic Video Object Removal

Panoramic video object removal aims to remove targets and associated effects while reconstructing occluded content across the viewing sphere. However, existing perspective-based methods struggle with panoramic videos, where ERP-induced seam discontinuities and polar distortions challenge both spherical reconstruction and target-effect association, while paired panoramic removal data remain scarce. To address these challenges, we construct PANOVOR, a paired panoramic video removal dataset containing 1.53M panoramic frames spanning 26.6 hours, with audited source-clean pairs and target masks. Building on PanoVOR, we propose PANOERASE, a unified model that incorporates spherical geometry into panoramic object and effect removal. PanoErase first learns panorama-aware completion from large-scale panoramic videos using SC-RoPE, which models spherical positional relationships. It is then refined with paired removal supervision through SphericalControl, which combines target-specific local evidence with panorama-wide context for effect-aware removal while preserving unrelated content. Experiments on PanoVOR-Eval and PanoVOR-Wild demonstrate consistent improvements over existing object removal methods, particularly in seam-crossing and polar regions. The dataset, code and models will be released publicly. The anonymous project website is available at https://panoerase-anonymous.pages.dev/.