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.

设计了一个稳定和精确的基于拖动的编辑框架 StableDrag，通过一种准确的点追踪方法和基于置信度的潜在增强策略，解决了点追踪不准确和运动监督不完整的问题，从而提高长距离操作的稳定性和操纵步骤中的优化潜在质量。通过在 DragBench 上进行广泛的定性实验和定量评估，实例化了 StableDrag-GAN 和 StableDrag-Diff 两种稳定的图像编辑模型。

StableDrag: 稳定点基图像编辑的拖动

StableDrag: Stable Dragging for Point-based Image Editing

To serve the intricate and varied demands of image editing, precise and
flexible manipulation of image content is indispensable. Recently, DragGAN has
achieved impressive editing results through point-based manipulation. However,
we have observed that DragGAN struggles with miss tracking, where DragGAN
encounters difficulty in effectively tracking the desired handle points, and
ambiguous tracking, where the tracked points are situated within other regions
that bear resemblance to the handle points. To deal with the above issues, we
propose FreeDrag, which adopts a feature-oriented approach to free the burden
on point tracking within the point-oriented methodology of DragGAN. The
FreeDrag incorporates adaptive template features, line search, and fuzzy
localization techniques to perform stable and efficient point-based image
editing. Extensive experiments demonstrate that our method is superior to the
DragGAN and enables stable point-based editing in challenging scenarios with
similar structures, fine details, or under multi-point targets.

DragGAN 在图像编辑中的准确和灵活的图像内容操纵对满足复杂和多样的需求至关重要。然而，我们观察到 DragGAN 在处理不准确的跟踪和模糊的跟踪方面存在困难。为了解决这些问题，我们提出了 FreeDrag，它在 DragGAN 的基础上采用了特征导向的方法来解放点追踪的负担，从而实现了稳定和高效的基于点的图像编辑。大量实验证明我们的方法在具有相似结构、精细细节或多点目标的挑战情景中优于 DragGAN，并实现了稳定的基于点的编辑。

FreeDrag：互动点基图像编辑不一定需要点跟踪

FreeDrag: Point Tracking is Not You Need for Interactive Point-based  Image Editing

Precise and controllable image editing is a challenging task that has
attracted significant attention. Recently, DragGAN enables an interactive
point-based image editing framework and achieves impressive editing results
with pixel-level precision. However, since this method is based on generative
adversarial networks (GAN), its generality is upper-bounded by the capacity of
the pre-trained GAN models. In this work, we extend such an editing framework
to diffusion models and propose DragDiffusion. By leveraging large-scale
pretrained diffusion models, we greatly improve the applicability of
interactive point-based editing in real world scenarios. While most existing
diffusion-based image editing methods work on text embeddings, DragDiffusion
optimizes the diffusion latent to achieve precise spatial control. Although
diffusion models generate images in an iterative manner, we empirically show
that optimizing diffusion latent at one single step suffices to generate
coherent results, enabling DragDiffusion to complete high-quality editing
efficiently. Extensive experiments across a wide range of challenging cases
(e.g., multi-objects, diverse object categories, various styles, etc.)
demonstrate the versatility and generality of DragDiffusion.

本文介绍了一种基于扩散模型的交互式基于点的图像编辑框架 DragDiffusion，并通过优化扩散模型潜在空间实现精确的空间控制。