To serve the intricate and varied demands of image editing, precise and flexible manipulation in image content is indispensable. Recently, Drag-based editing methods have gained impressive performance. However, these methods predominantly center on point dragging, resulting in two noteworthy drawbacks, namely "miss tracking", where difficulties arise in accurately tracking the predetermined handle points, and "ambiguous tracking", where tracked points are potentially positioned in wrong regions that closely resemble the handle points. To address the above issues, we propose FreeDrag, a feature dragging methodology designed to free the burden on point tracking. The FreeDrag incorporates two key designs, i.e., template feature via adaptive updating and line search with backtracking, the former improves the stability against drastic content change by elaborately controls feature updating scale after each dragging, while the latter alleviates the misguidance from similar points by actively restricting the search area in a line. These two technologies together contribute to a more stable semantic dragging with higher efficiency. Comprehensive experimental results substantiate that our approach significantly outperforms pre-existing methodologies, offering reliable point-based editing even in various complex scenarios.

翻译：为满足图像编辑中复杂多变的需求，对图像内容进行精确且灵活的操作不可或缺。近年来，基于拖动的编辑方法取得了令人瞩目的性能。然而，这些方法主要聚焦于点拖动，导致两个显著缺陷：一是“跟踪丢失”，即难以准确跟踪预定义的操控点；二是“模糊跟踪”，即跟踪点可能错误地定位在与操控点高度相似的非目标区域。针对上述问题，我们提出FreeDrag——一种旨在减轻点跟踪负担的特征拖动方法。FreeDrag包含两项关键设计：基于自适应更新的模板特征和结合回溯的线搜索。前者通过精细控制每次拖动后的特征更新尺度，提升了对剧烈内容变化的稳定性；后者通过主动将搜索区域限制在一条线上，缓解了相似点的误导。这两项技术共同实现了更稳定、高效的语义拖动。综合实验结果表明，我们的方法显著优于现有技术，即使在多种复杂场景下也能提供可靠的基于点的编辑。