FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
Drag-based editing allows precise object manipulation through point-based
control, offering user convenience. However, current methods often suffer from
a geometric inconsistency problem by focusing exclusively on matching
user-defined points, neglecting the broader geometry and leading to artifacts
or unstable edits. We propose FlowDrag, which leverages geometric information
for more accurate and coherent transformations. Our approach constructs a 3D
mesh from the image, using an energy function to guide mesh deformation based
on user-defined drag points. The resulting mesh displacements are projected
into 2D and incorporated into a UNet denoising process, enabling precise
handle-to-target point alignment while preserving structural integrity.
Additionally, existing drag-editing benchmarks provide no ground truth, making
it difficult to assess how accurately the edits match the intended
transformations. To address this, we present VFD (VidFrameDrag) benchmark
dataset, which provides ground-truth frames using consecutive shots in a video
dataset. FlowDrag outperforms existing drag-based editing methods on both VFD
Bench and DragBench.
