Combing Text-based and Drag-based Editing for Precise and Flexible Image Editing

• URL: http://arxiv.org/abs/2410.03097v1
• Date: Fri, 4 Oct 2024 02:46:09 GMT
• Title: Combing Text-based and Drag-based Editing for Precise and Flexible Image Editing
• Authors: Ziqi Jiang, Zhen Wang, Long Chen,
• Abstract summary: We propose textbfCLIPDrag, a novel image editing method that combines text and drag signals for precise and ambiguity-free manipulations. CLIPDrag outperforms existing single drag-based methods or text-based methods.
• Score: 9.398831289389749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precise and flexible image editing remains a fundamental challenge in computer vision. Based on the modified areas, most editing methods can be divided into two main types: global editing and local editing. In this paper, we choose the two most common editing approaches (ie text-based editing and drag-based editing) and analyze their drawbacks. Specifically, text-based methods often fail to describe the desired modifications precisely, while drag-based methods suffer from ambiguity. To address these issues, we proposed \textbf{CLIPDrag}, a novel image editing method that is the first to combine text and drag signals for precise and ambiguity-free manipulations on diffusion models. To fully leverage these two signals, we treat text signals as global guidance and drag points as local information. Then we introduce a novel global-local motion supervision method to integrate text signals into existing drag-based methods by adapting a pre-trained language-vision model like CLIP. Furthermore, we also address the problem of slow convergence in CLIPDrag by presenting a fast point-tracking method that enforces drag points moving toward correct directions. Extensive experiments demonstrate that CLIPDrag outperforms existing single drag-based methods or text-based methods.

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