SphereDrag: Spherical Geometry-Aware Panoramic Image Editing
This work addresses panoramic image editing for applications in VR and 360-degree imaging, representing a novel method for a known bottleneck in the field.
The paper tackles panoramic image editing by addressing challenges like boundary discontinuity and uneven pixel density, proposing SphereDrag with methods such as adaptive reprojection and spherical search region tracking, which achieves up to 10.5% relative improvement in geometric consistency and image quality.
Image editing has made great progress on planar images, but panoramic image editing remains underexplored. Due to their spherical geometry and projection distortions, panoramic images present three key challenges: boundary discontinuity, trajectory deformation, and uneven pixel density. To tackle these issues, we propose SphereDrag, a novel panoramic editing framework utilizing spherical geometry knowledge for accurate and controllable editing. Specifically, adaptive reprojection (AR) uses adaptive spherical rotation to deal with discontinuity; great-circle trajectory adjustment (GCTA) tracks the movement trajectory more accurate; spherical search region tracking (SSRT) adaptively scales the search range based on spherical location to address uneven pixel density. Also, we construct PanoBench, a panoramic editing benchmark, including complex editing tasks involving multiple objects and diverse styles, which provides a standardized evaluation framework. Experiments show that SphereDrag gains a considerable improvement compared with existing methods in geometric consistency and image quality, achieving up to 10.5% relative improvement.