KriDrag: Spatially Autocorrelated Conflict-Free Drag-Based Image Editing

Drag-based image editing provides an intuitive interface for spatial image editing through a small number of user-specified point movements. Existing methods primarily rely on interpolation to construct the displacement field, paying insufficient attention to the role of explicit spatial modeling. Moreover, the displacement fields induced by multiple drags are directly aggregated, often overlooking their mutual conflicts. In this work, we introduce KriDrag, a spatially autocorrelated and conflict-free framework for drag-based image editing. KriDrag constructs a Covariance-Guided Displacement Field, where each displacement influences its surrounding positions through spatial autocorrelation. For multiple drags, KriDrag constructs a continuous and smooth displacement field that preserves coherent motion across neighboring regions while suppressing conflicting displacements between drags. Experiments on DragBench and ReD-Bench demonstrate the efficiency and effectiveness of our method for drag-based image editing, particularly on multi-drag benchmarks.