Abstract
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.
Abstract (translated)
在本文中，我们提出了GoodDrag，一种改进拖拽编辑稳定性和平衡图像质量的新方法。与现有方法遇到累积扰动并经常导致扭曲不同，GoodDrag引入了一个交替在扩散过程中进行拖动和去噪操作的AlDD框架，有效提高了结果的保真度。我们还提出了一个保持起始点原始特征的信息保留运动监督操作，用于精确操作和伪影减少。此外，我们还通过引入一个新的数据集Drag100和开发专门的质量评估指标Dragging Accuracy Index和Gemini Score，对拖拽编辑进行了基准测试。大量的实验证明，与最先进的拖拽编辑方法相比，GoodDrag在质量和数量上都有优势。项目页面URL是https://url。
URL
https://arxiv.org/abs/2404.07206
