ThinkDrag: Semantic Drag-Based Image Editing with Visual Reasoning
ThinkDrag uses visual reasoning so drag-based image edits follow semantic changes, not only geometry.
ThinkDrag is a multimodal framework for drag-based image editing that infers object- and scene-level transformations from sparse handle and target points instead of treating drags as geometric displacements. It can emit an explicit reasoning trace before generation and uses Drag Endpoint Markers, derived from cross-image attention analysis, to ground each drag. The authors built a supervised dataset of semantic drag edits with reasoning traces and DragBench++, a benchmark of difficult cases with reference solutions. They report state-of-the-art quality and plausibility while keeping point-following precision competitive.
- ThinkDrag treats drag points as semantic edits rather than pure geometry.
- An optional reasoning trace explains ambiguous drags before generation.
- Drag Endpoint Markers add explicit image-space correspondence for each drag.
- DragBench++ evaluates difficult edits against reference solutions.
- Reported results improve quality and plausibility with competitive point accuracy.
From the original article
1 min read at huggingface.co
Drag-based image editing provides an intuitive interface for spatially controlled image manipulation by allowing users to specify handle and target points. Existing methods have made substantial progress through optimization-based, one-step, and feed-forward formulations, but they often interpret drag instructions as geometric displacements.
By Armando Fortes, Tianyi Wei, Chindanai Trakantannarong · huggingface.co
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