TDEdit：面向文本-拖拽引导图像编辑的统一扩散框架
TDEdit: A Unified Diffusion Framework for Text-Drag Guided Image Manipulation
AI总结：
本文提出统一扩散框架TDEdit，通过点云确定性拖拽和拖拽-文本引导去噪融合文本与拖拽编辑优势，实现高保真且灵活可控的图像编辑。
摘要：
本文探索了在文本与拖拽交互联合控制下的图像编辑。尽管近期文本驱动和拖拽驱动的编辑方法取得了显著进展，但它们存在互补性的局限：文本驱动方法擅长纹理操控但缺乏精确的空间控制，而拖拽驱动方法主要修改形状和结构，缺乏细粒度的纹理引导。为解决这些局限，我们提出了一种统一的基于扩散的联合拖拽-文本图像编辑框架，融合了两种范式的优势。该框架引入了两项关键创新：（1）点云确定性拖拽（Point-Cloud Deterministic Drag），通过3D特征映射增强潜空间布局控制；（2）拖拽-文本引导去噪（Drag-Text Guided Denoising），在去噪过程中动态平衡拖拽条件与文本条件的影响。值得注意的是，我们的模型支持灵活的编辑模式——可仅用文本、仅用拖拽或组合条件运行——同时在每种设置下均保持强劲性能。大量定量和定性实验表明，我们的方法不仅能实现高保真的联合编辑，而且能达到或超越专门的仅文本或仅拖拽方法的性能，为可控图像编辑提供了一种通用且可推广的解决方案。代码将公开发布，以复现本工作中展示的所有结果。
英文摘要：
This paper explores image editing under the joint control of text and drag interactions. While recent advances in text-driven and drag-driven editing have achieved remarkable progress, they suffer from complementary limitations: text-driven methods excel in texture manipulation but lack precise spatial control, whereas drag-driven approaches primarily modify shape and structure without fine-grained texture guidance. To address these limitations, we propose a unified diffusion-based framework for joint drag-text image editing, integrating the strengths of both paradigms. Our framework introduces two key innovations: (1) Point-Cloud Deterministic Drag, which enhances latent-space layout control through 3D feature mapping, and (2) Drag-Text Guided Denoising, dynamically balancing the influence of drag and text conditions during denoising. Notably, our model supports flexible editing modes - operating with text-only, drag-only, or combined conditions - while maintaining strong performance in each setting. Extensive quantitative and qualitative experiments demonstrate that our method not only achieves high-fidelity joint editing but also matches or surpasses the performance of specialized text-only or drag-only approaches, establishing a versatile and generalizable solution for controllable image manipulation. Code will be made publicly available to reproduce all results presented in this work.
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。
