SphereDrag: Spherical Geometry-Aware Panoramic Image Editing

TL;DR

SphereDrag introduces a spherical geometry-aware framework for panoramic image editing, effectively addressing boundary discontinuity, trajectory deformation, and pixel density issues, with a new benchmark for evaluation.

Contribution

The paper presents SphereDrag, a novel editing framework leveraging spherical geometry, and PanoBench, a benchmark for comprehensive evaluation of panoramic editing methods.

Findings

Up to 10.5% improvement in geometric consistency and image quality.

Effective handling of boundary discontinuity and trajectory deformation.

Introduction of a standardized panoramic editing benchmark.

Abstract

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.