CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing

• URL: http://arxiv.org/abs/2506.18438v1
• Date: Mon, 23 Jun 2025 09:19:38 GMT
• Title: CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing
• Authors: Dinh-Khoi Vo, Thanh-Toan Do, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le,
• Abstract summary: Context-Preserving Adaptive Manipulation (CPAM) is a novel framework for complicated, non-rigid real image editing.<n>We develop a preservation adaptation module that adjusts self-attention mechanisms to preserve and independently control the object and background effectively.<n>We also introduce various mask-guidance strategies to facilitate diverse image manipulation tasks in a simple manner.
• Score: 24.68304617869157
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Editing natural images using textual descriptions in text-to-image diffusion models remains a significant challenge, particularly in achieving consistent generation and handling complex, non-rigid objects. Existing methods often struggle to preserve textures and identity, require extensive fine-tuning, and exhibit limitations in editing specific spatial regions or objects while retaining background details. This paper proposes Context-Preserving Adaptive Manipulation (CPAM), a novel zero-shot framework for complicated, non-rigid real image editing. Specifically, we propose a preservation adaptation module that adjusts self-attention mechanisms to preserve and independently control the object and background effectively. This ensures that the objects' shapes, textures, and identities are maintained while keeping the background undistorted during the editing process using the mask guidance technique. Additionally, we develop a localized extraction module to mitigate the interference with the non-desired modified regions during conditioning in cross-attention mechanisms. We also introduce various mask-guidance strategies to facilitate diverse image manipulation tasks in a simple manner. Extensive experiments on our newly constructed Image Manipulation BenchmArk (IMBA), a robust benchmark dataset specifically designed for real image editing, demonstrate that our proposed method is the preferred choice among human raters, outperforming existing state-of-the-art editing techniques.

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