DreamFuse: Adaptive Image Fusion with Diffusion Transformer

• URL: http://arxiv.org/abs/2504.08291v1
• Date: Fri, 11 Apr 2025 06:49:33 GMT
• Title: DreamFuse: Adaptive Image Fusion with Diffusion Transformer
• Authors: Junjia Huang, Pengxiang Yan, Jiyang Liu, Jie Wu, Zhao Wang, Yitong Wang, Liang Lin, Guanbin Li,
• Abstract summary: We propose an iterative human-in-the-loop data generation pipeline to generate image fusion datasets.<n>We introduce DreamFuse, a novel approach to generate consistent and fused images with both foreground and background information.<n>DreamFuse achieves harmonious fusion while generalizing to text-driven editing of the fused results.
• Score: 85.69622262632066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image fusion seeks to seamlessly integrate foreground objects with background scenes, producing realistic and harmonious fused images. Unlike existing methods that directly insert objects into the background, adaptive and interactive fusion remains a challenging yet appealing task. It requires the foreground to adjust or interact with the background context, enabling more coherent integration. To address this, we propose an iterative human-in-the-loop data generation pipeline, which leverages limited initial data with diverse textual prompts to generate fusion datasets across various scenarios and interactions, including placement, holding, wearing, and style transfer. Building on this, we introduce DreamFuse, a novel approach based on the Diffusion Transformer (DiT) model, to generate consistent and harmonious fused images with both foreground and background information. DreamFuse employs a Positional Affine mechanism to inject the size and position of the foreground into the background, enabling effective foreground-background interaction through shared attention. Furthermore, we apply Localized Direct Preference Optimization guided by human feedback to refine DreamFuse, enhancing background consistency and foreground harmony. DreamFuse achieves harmonious fusion while generalizing to text-driven attribute editing of the fused results. Experimental results demonstrate that our method outperforms state-of-the-art approaches across multiple metrics.

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