DreamFuse: Toward Realistic and Seamless Image Fusion Across Diverse Scenarios

Image fusion seeks to seamlessly integrate foreground objects with background scenes, producing realistic and harmonious fused images. While existing methods often insert objects directly, adaptive and interactive fusion-requiring contextual adaptation and foreground-background interplay-remains a challenging yet critical task. To address this, we first propose a pipeline for generating high-quality fusion data. By combining iterative in-context learning with existing tools, we curate a diverse cross-scene dataset supporting three core tasks: object integration, replacement, and attribute-referenced editing. Leveraging this, we introduce DreamFuse, a unified diffusion-based approach that jointly optimizes these capabilities. DreamFuse exploits the Diffusion Transformer (DiT) architecture, using its attention mechanism to extract and align foreground-background features for coherent fusion. For flexible control, we incorporate a Positional Affine mechanism, enabling precise spatial and scale adjustments while supporting diverse text-driven fusion. Furthermore, we employ Localized Direct Preference Optimization (L-DPO), refining the model via human feedback to enhance harmony and consistency. Extensive experimental results demonstrate DreamFuse's superiority over state-of-the-art approaches across multiple metrics.