CannyDrag: Unified Point, Line, and Region Drag Editing via Canny Guidance

Drag-based image editing offers an intuitive method to control spatial changes through handle-target point pairs, but sparse point instructions often lead to ambiguous editing intentions. The same point movement may correspond to local deformation, line bending, or region-level transformation, while existing motion supervision mainly constrains local neighborhoods around handle points. To address this limitation, we propose CannyDrag, a canny-guided drag editing framework that extends point-based control to point-, line-, and region-level manipulation. CannyDrag first introduces canny guidance during inversion to keep the inverted latent compatible with the later denoising trajectory. It then manipulates the canny map according to the selected task type and injects the transformed canny condition progressively during denoising. This design converts sparse drag instructions into task-specific structural guidance and helps align the editing process with user intentions. Experiments on point-, line-, and region-level drag tasks demonstrate that CannyDrag improves spatial accuracy while maintaining competitive image fidelity.