In this paper, we introduce GoodDrag, a novel approach to improve the
stability and image quality of drag editing. Unlike existing methods that
struggle with accumulated perturbations and often result in distortions,
GoodDrag introduces an AlDD framework that alternates between drag and
denoising operations within the diffusion process, effectively improving the
fidelity of the result. We also propose an information-preserving motion
supervision operation that maintains the original features of the starting
point for precise manipulation and artifact reduction. In addition, we
contribute to the benchmarking of drag editing by introducing a new dataset,
Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy
Index and Gemini Score, utilizing Large Multimodal Models. Extensive
experiments demonstrate that the proposed GoodDrag compares favorably against
the state-of-the-art approaches both qualitatively and quantitatively. The
project page is this https URL

介绍了 GoodDrag，一种改善拖拽编辑稳定性和图像质量的新方法。通过引入 AlDD 框架进行扩散过程中的拖拽和去噪操作交替，有效提高结果的保真度。提出了信息保持的运动监督操作，以保持精确操作和减少伪影。除此之外，通过引入 Drag100 数据集和开发专用的质量评估指标，Dragging Accuracy Index 和 Gemini Score 来贡献拖拽编辑的基准测试。广泛的实验证明，所提出的 GoodDrag 在定性和定量上与最先进的方法相比具有优势。

GoodDrag：面向扩散模型的拖拽编辑良好实践

GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models

Synthesizing visual content that meets users' needs often requires flexible
and precise controllability of the pose, shape, expression, and layout of the
generated objects. Existing approaches gain controllability of generative
adversarial networks (GANs) via manually annotated training data or a prior 3D
model, which often lack flexibility, precision, and generality. In this work,
we study a powerful yet much less explored way of controlling GANs, that is, to
"drag" any points of the image to precisely reach target points in a
user-interactive manner, as shown in Fig.1. To achieve this, we propose
DragGAN, which consists of two main components: 1) a feature-based motion
supervision that drives the handle point to move towards the target position,
and 2) a new point tracking approach that leverages the discriminative
generator features to keep localizing the position of the handle points.
Through DragGAN, anyone can deform an image with precise control over where
pixels go, thus manipulating the pose, shape, expression, and layout of diverse
categories such as animals, cars, humans, landscapes, etc. As these
manipulations are performed on the learned generative image manifold of a GAN,
they tend to produce realistic outputs even for challenging scenarios such as
hallucinating occluded content and deforming shapes that consistently follow
the object's rigidity. Both qualitative and quantitative comparisons
demonstrate the advantage of DragGAN over prior approaches in the tasks of
image manipulation and point tracking. We also showcase the manipulation of
real images through GAN inversion.

本论文提出了 DragGAN，一种基于运动监督和点追踪的生成对抗网络控制方式，旨在实现对图像姿势、形状、表情和布局的精确操作和灵活掌控。实验结果表明，在图像操作和点追踪方面，DragGAN 比以前的方法更具优势。