Achieving Seamless Camouflage: Attention Fusion Diffusion Model for Image Synthesis

Camouflage image generation plays a vital role in various research fields. Current methods typically rely on manually selecting and blending objects with backgrounds, which often produce incongruous combinations where the object does not seamlessly integrate with the background, leading to unrealistic and unnatural visual outcomes. To address these challenges, we introduce a novel Attention Fusion Diffusion Model (AFDM) designed to generate realistic camouflage images from a single input image containing an object and its surrounding background. The AFDM framework is comprised of two essential components: an Attention Fusion Module, which adeptly integrates object features with surrounding background information to produce convincingly camouflaged objects, and a content guidance strategy designed to mitigate content drift during the fusion process, thereby ensuring that the camouflaged image remains faithfully aligned with the original content. In addition, we build the outdoor Solidier Dataset(OSD) for advancing camouflage target recognization and in-depth research on this topic. Extensive experiments and user studies demonstrate the performance of our method in camouflage image generation and its potential to enhance image segmentation-related fields. Our code and dataset will be available at https://github.com/xhxhzhz/AFDM.