通过预测运动实现动态拖拽编辑，解决传统方法跟踪失误问题。
DynaDrag: Dynamic Drag-Style Image Editing by Motion Prediction
- 采用预测-移动框架，迭代预测并引导控制点运动
- 在人脸与人体数据集上实现更精准的像素级编辑
- 动态调整有效控制点，提升编辑灵活性与准确性
为实现像素级图像编辑，基于点或轨迹条件的拖拽式编辑受到广泛关注。以往多数方法采用“移动-跟踪”框架，存在漏跟踪和模糊跟踪等难题。其他框架则面临源图与目标图间差距大、中间点不合理等问题，导致编辑能力不足。为此，本文提出首个基于“预测-移动”框架的拖拽方法DynaDrag。该方法在每轮迭代中先预测控制点的运动位置，再通过运动监督进行实际拖动，并动态调整有效控制点以进一步提升性能。在人脸与人体数据集上的实验表明，该方法显著优于现有工作。
原文摘要 · Abstract (English)
To achieve pixel-level image manipulation, drag-style image editing which edits images using points or trajectories as conditions is attracting widespread attention. Most previous methods follow move-and-track framework, in which miss tracking and ambiguous tracking are unavoidable challenging issues. Other methods under different frameworks suffer from various problems like the huge gap between source image and target edited image as well as unreasonable intermediate point which can lead to low editability. To avoid these problems, we propose DynaDrag, the first dragging method under predict-and-move framework. In DynaDrag, Motion Prediction and Motion Supervision are performed iteratively. In each iteration, Motion Prediction first predicts where the handle points should move, and then Motion Supervision drags them accordingly. We also propose to dynamically adjust the valid handle points to further improve the performance. Experiments on face and human datasets showcase the superiority over previous works.
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