Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner

Understand

Flexible and accurate drag-based editing is a challenging task that has recently garnered significant attention.

• Current methods typically model this problem as automatically learning "how to drag" through point dragging and often produce one deterministic estimation, which presents two key limitations: 1) Overlooking the inherently ill-posed nature of drag-based editing, where multiple results may correspond to a given input, as illustrated in Fig.1; 2) Ignoring the constraint of image quality, which may lead to unexpected distortion.
• To alleviate this, we propose LucidDrag, which shifts the focus from "how to drag" to "what-then-how" paradigm.
• LucidDrag comprises an intention reasoner and a collaborative guidance sampling mechanism.