Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing
  via Bidirectional Warping
Drag-based image editing has emerged as a powerful paradigm for intuitive
image manipulation. However, existing approaches predominantly rely on
manipulating the latent space of generative models, leading to limited
precision, delayed feedback, and model-specific constraints. Accordingly, we
present Inpaint4Drag, a novel framework that decomposes drag-based editing into
pixel-space bidirectional warping and image inpainting. Inspired by elastic
object deformation in the physical world, we treat image regions as deformable
materials that maintain natural shape under user manipulation. Our method
achieves real-time warping previews (0.01s) and efficient inpainting (0.3s) at
512x512 resolution, significantly improving the interaction experience compared
to existing methods that require minutes per edit. By transforming drag inputs
directly into standard inpainting formats, our approach serves as a universal
adapter for any inpainting model without architecture modification,
automatically inheriting all future improvements in inpainting technology.
Extensive experiments demonstrate that our method achieves superior visual
quality and precise control while maintaining real-time performance. Project
page: https://visual-ai.github.io/inpaint4drag/
68bebd40c123124955ef61c6
Jingyi Lu
claimed_verified
2025-09-09T13:49:32.977Z
65260483b5cccc82eab1a559
/avatars/bcda44ab0b46f394b18ad4469154f38f.svg
LU Jingyi
user
LuJingyi
68bebd40c123124955ef61c7
Kai Han
Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing
  via Bidirectional Warping
Drag-based image editing has emerged as a powerful paradigm for intuitive
image manipulation. However, existing approaches predominantly rely on
manipulating the latent space of generative models, leading to limited
precision, delayed feedback, and model-specific constraints. Accordingly, we
present Inpaint4Drag, a novel framework that decomposes drag-based editing into
pixel-space bidirectional warping and image inpainting. Inspired by elastic
object deformation in the physical world, we treat image regions as deformable
materials that maintain natural shape under user manipulation. Our method
achieves real-time warping previews (0.01s) and efficient inpainting (0.3s) at
512x512 resolution, significantly improving the interaction experience compared
to existing methods that require minutes per edit. By transforming drag inputs
directly into standard inpainting formats, our approach serves as a universal
adapter for any inpainting model without architecture modification,
automatically inheriting all future improvements in inpainting technology.
Extensive experiments demonstrate that our method achieves superior visual
quality and precise control while maintaining real-time performance. Project
page: https://visual-ai.github.io/inpaint4drag/
Inpaint4Drag enhances drag-based image editing by decomposing it into pixel-space warping and inpainting, offering real-time performance and superior visual quality.
drag-based editing latent space generative models pixel-space bidirectional warping image inpainting elastic object deformation real-time warping real-time inpainting inpainting formats universal adapter