title: InstantDrag: Improving Interactivity in Drag-based Image Editing
summary: Drag-based image editing has recently gained popularity for its interactivity
  and precision. However, despite the ability of text-to-image models to generate
  samples within a second, drag editing still lags behind due to the challenge of
  accurately reflecting user interaction while maintaining image content. Some
  existing approaches rely on computationally intensive per-image optimization or
  intricate guidance-based methods, requiring additional inputs such as masks for
  movable regions and text prompts, thereby compromising the interactivity of the
  editing process. We introduce InstantDrag, an optimization-free pipeline that
  enhances interactivity and speed, requiring only an image and a drag
  instruction as input. InstantDrag consists of two carefully designed networks:
  a drag-conditioned optical flow generator (FlowGen) and an optical
  flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion
  dynamics for drag-based image editing in real-world video datasets by
  decomposing the task into motion generation and motion-conditioned image
  generation. We demonstrate InstantDrag's capability to perform fast,
  photo-realistic edits without masks or text prompts through experiments on
  facial video datasets and general scenes. These results highlight the
  efficiency of our approach in handling drag-based image editing, making it a
  promising solution for interactive, real-time applications.
paper_authors._id: 66e836da9ebf20e0639f3541
paper_authors.name: Joonghyuk Shin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-17T07:11:47.682Z
paper_authors._id: 631074d895c34b95407945f0
paper_authors.avatarUrl: /avatars/699baf06ec818650dec5752aca87c5b4.svg
paper_authors.fullname: Joonghyuk Shin
paper_authors.type: user
paper_authors.user: alex4727
paper_authors._id: 66e836da9ebf20e0639f3542
paper_authors.name: Daehyeon Choi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-15T08:50:46.969Z
paper_authors._id: 6616702547ea6347974667e5
paper_authors.avatarUrl: /avatars/6bf95e50ba19df163ef89867ed63fecc.svg
paper_authors.fullname: Daehyeon Choi
paper_authors.type: user
paper_authors.user: daehyeonchoi
paper_authors._id: 66e836da9ebf20e0639f3543
paper_authors.name: Jaesik Park
paper_title: InstantDrag: Improving Interactivity in Drag-based Image Editing
paper_summary: Drag-based image editing has recently gained popularity for its interactivity
  and precision. However, despite the ability of text-to-image models to generate
  samples within a second, drag editing still lags behind due to the challenge of
  accurately reflecting user interaction while maintaining image content. Some
  existing approaches rely on computationally intensive per-image optimization or
  intricate guidance-based methods, requiring additional inputs such as masks for
  movable regions and text prompts, thereby compromising the interactivity of the
  editing process. We introduce InstantDrag, an optimization-free pipeline that
  enhances interactivity and speed, requiring only an image and a drag
  instruction as input. InstantDrag consists of two carefully designed networks:
  a drag-conditioned optical flow generator (FlowGen) and an optical
  flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion
  dynamics for drag-based image editing in real-world video datasets by
  decomposing the task into motion generation and motion-conditioned image
  generation. We demonstrate InstantDrag's capability to perform fast,
  photo-realistic edits without masks or text prompts through experiments on
  facial video datasets and general scenes. These results highlight the
  efficiency of our approach in handling drag-based image editing, making it a
  promising solution for interactive, real-time applications.
paper_ai_summary: InstantDrag, an optimization-free pipeline with two networks, performs fast, photo-realistic drag-based image editing without requiring masks or text prompts.
paper_ai_keywords: drag-conditioned optical flow optical flow-conditioned diffusion model motion generation motion-conditioned image generation