DragNeXt: Rethinking Drag-Based Image Editing

• URL: http://arxiv.org/abs/2506.07611v1
• Date: Mon, 09 Jun 2025 10:24:29 GMT
• Title: DragNeXt: Rethinking Drag-Based Image Editing
• Authors: Yuan Zhou, Junbao Zhou, Qingshan Xu, Kesen Zhao, Yuxuan Wang, Hao Fei, Richang Hong, Hanwang Zhang,
• Abstract summary: Drag-Based Image Editing (DBIE) allows users to manipulate images by directly dragging objects within them.<n>It faces two key challenges: (emphtextcolormagentaii) point-based drag is often highly ambiguous and difficult to align with users' intentions.<n>We propose a simple-yet-effective editing framework, dubbed textcolorSkyBluetextbfDragNeXt.
• Score: 81.9430401732008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drag-Based Image Editing (DBIE), which allows users to manipulate images by directly dragging objects within them, has recently attracted much attention from the community. However, it faces two key challenges: (\emph{\textcolor{magenta}{i}}) point-based drag is often highly ambiguous and difficult to align with users' intentions; (\emph{\textcolor{magenta}{ii}}) current DBIE methods primarily rely on alternating between motion supervision and point tracking, which is not only cumbersome but also fails to produce high-quality results. These limitations motivate us to explore DBIE from a new perspective -- redefining it as deformation, rotation, and translation of user-specified handle regions. Thereby, by requiring users to explicitly specify both drag areas and types, we can effectively address the ambiguity issue. Furthermore, we propose a simple-yet-effective editing framework, dubbed \textcolor{SkyBlue}{\textbf{DragNeXt}}. It unifies DBIE as a Latent Region Optimization (LRO) problem and solves it through Progressive Backward Self-Intervention (PBSI), simplifying the overall procedure of DBIE while further enhancing quality by fully leveraging region-level structure information and progressive guidance from intermediate drag states. We validate \textcolor{SkyBlue}{\textbf{DragNeXt}} on our NextBench, and extensive experiments demonstrate that our proposed method can significantly outperform existing approaches. Code will be released on github.

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