GoodDrag：迈向基于扩散模型的拖拽编辑良好实践
GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models
AI总结：
针对扩散模型拖拽编辑中的累积扰动与失真问题，提出 GoodDrag，通过 AlDD 交替拖拽去噪和信息保持运动监督提升保真度，并构建 Drag100 及两项多模态评测指标。
摘要：
本文提出 GoodDrag，这是一种提升拖拽编辑稳定性和图像质量的新颖方法。现有方法难以处理累积扰动，并常导致失真；与之不同，GoodDrag 引入 AlDD 框架，在扩散过程中交替执行拖拽和去噪操作，有效提升结果保真度。我们还提出一种信息保持的运动监督操作，可维持起点的原始特征，从而实现精确操控并减少伪影。此外，我们通过引入新数据集 Drag100，并利用大型多模态模型开发专门的质量评估指标 Dragging Accuracy Index 和 Gemini Score，为拖拽编辑的基准评测作出贡献。大量实验表明，所提出的 GoodDrag 在定性和定量方面均优于最先进方法。项目页面为 https://gooddrag.github.io。
英文摘要：
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 https://gooddrag.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
