title: StableDrag: Stable Dragging for Point-based Image Editing
summary: Point-based image editing has attracted remarkable attention since the
  emergence of DragGAN. Recently, DragDiffusion further pushes forward the
  generative quality via adapting this dragging technique to diffusion models.
  Despite these great success, this dragging scheme exhibits two major drawbacks,
  namely inaccurate point tracking and incomplete motion supervision, which may
  result in unsatisfactory dragging outcomes. To tackle these issues, we build a
  stable and precise drag-based editing framework, coined as StableDrag, by
  designing a discirminative point tracking method and a confidence-based latent
  enhancement strategy for motion supervision. The former allows us to precisely
  locate the updated handle points, thereby boosting the stability of long-range
  manipulation, while the latter is responsible for guaranteeing the optimized
  latent as high-quality as possible across all the manipulation steps. Thanks to
  these unique designs, we instantiate two types of image editing models
  including StableDrag-GAN and StableDrag-Diff, which attains more stable
  dragging performance, through extensive qualitative experiments and
  quantitative assessment on DragBench.
paper_authors._id: 65ea8686cdc26f962fb572e0
paper_authors.name: Yutao Cui
paper_authors._id: 65ea8686cdc26f962fb572e1
paper_authors.name: Xiaotong Zhao
paper_authors._id: 65ea8686cdc26f962fb572e2
paper_authors.name: Guozhen Zhang
paper_authors._id: 65ea8686cdc26f962fb572e3
paper_authors.name: Shengming Cao
paper_authors._id: 65ea8686cdc26f962fb572e4
paper_authors.name: Kai Ma
paper_authors._id: 65ea8686cdc26f962fb572e5
paper_authors.name: Limin Wang
paper_title: StableDrag: Stable Dragging for Point-based Image Editing
paper_summary: Point-based image editing has attracted remarkable attention since the
  emergence of DragGAN. Recently, DragDiffusion further pushes forward the
  generative quality via adapting this dragging technique to diffusion models.
  Despite these great success, this dragging scheme exhibits two major drawbacks,
  namely inaccurate point tracking and incomplete motion supervision, which may
  result in unsatisfactory dragging outcomes. To tackle these issues, we build a
  stable and precise drag-based editing framework, coined as StableDrag, by
  designing a discirminative point tracking method and a confidence-based latent
  enhancement strategy for motion supervision. The former allows us to precisely
  locate the updated handle points, thereby boosting the stability of long-range
  manipulation, while the latter is responsible for guaranteeing the optimized
  latent as high-quality as possible across all the manipulation steps. Thanks to
  these unique designs, we instantiate two types of image editing models
  including StableDrag-GAN and StableDrag-Diff, which attains more stable
  dragging performance, through extensive qualitative experiments and
  quantitative assessment on DragBench.
paper_ai_summary: StableDrag, a drag-based image editing framework, enhances dragging performance through precise point tracking and confident latent enhancement, applied to both GAN and diffusion models.
paper_ai_keywords: drag-based image editing DragGAN DragDiffusion point tracking motion supervision latent enhancement long-range manipulation StableDrag-GAN StableDrag-Diff DragBench