To serve the intricate and varied demands of image editing, precise and flexible manipulation of image content is indispensable. Recently, DragGAN has achieved impressive editing results through point-based manipulation. However, we have observed that DragGAN struggles with miss tracking, where DragGAN encounters difficulty in effectively tracking the desired handle points, and ambiguous tracking, where the tracked points are situated within other regions that bear resemblance to the handle points. To deal with the above issues, we propose FreeDrag, which adopts a feature-oriented approach to free the burden on point tracking within the point-oriented methodology of DragGAN. The FreeDrag incorporates adaptive template features, line search, and fuzzy localization techniques to perform stable and efficient point-based image editing. Extensive experiments demonstrate that our method is superior to the DragGAN and enables stable point-based editing in challenging scenarios with similar structures, fine details, or under multi-point targets.

翻译：为满足图像编辑复杂多样的需求，对图像内容进行精确且灵活的操作不可或缺。近期，DragGAN通过基于点的操控取得了令人瞩目的编辑效果。然而，我们观察到DragGAN存在跟踪缺失问题（即难以有效跟踪目标控制点）以及模糊跟踪问题（即跟踪点位于与控制点相似的其他区域）。针对上述问题，我们提出FreeDrag方法，该采用特征导向的策略，以减轻DragGAN基于点方法中对于点跟踪的依赖。FreeDrag融合自适应模板特征、线性搜索及模糊定位技术，实现了稳定高效的基于点图像编辑。大量实验证明，本方法优于DragGAN，能够在包含相似结构、精细细节或多点目标的复杂场景中实现稳定的基于点编辑。