StableDrag: Stable Dragging for Point-based Image Editing
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.
65ea8686cdc26f962fb572e0
Yutao Cui
65ea8686cdc26f962fb572e1
Xiaotong Zhao
65ea8686cdc26f962fb572e2
Guozhen Zhang
65ea8686cdc26f962fb572e3
Shengming Cao
65ea8686cdc26f962fb572e4
Kai Ma
65ea8686cdc26f962fb572e5
Limin Wang
StableDrag: Stable Dragging for Point-based Image Editing
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.
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.
drag-based image editing DragGAN DragDiffusion point tracking motion supervision latent enhancement long-range manipulation StableDrag-GAN StableDrag-Diff DragBench