Camouflaged image generation is emerging as a solution to data scarcity in camouflaged vision perception, offering a cost-effective alternative to data collection and labeling. Recently, the state-of-the-art approach successfully generates camouflaged images using only foreground objects. However, it faces two critical weaknesses: 1) the background knowledge does not integrate effectively with foreground features, resulting in a lack of foreground-background coherence (e.g., color discrepancy); 2) the generation process does not prioritize the fidelity of foreground objects, which leads to distortion, particularly for small objects. To address these issues, we propose a Foreground-Aware Camouflaged Image Generation (FACIG) model. Specifically, we introduce a Foreground-Aware Feature Integration Module (FAFIM) to strengthen the integration between foreground features and background knowledge. In addition, a Foreground-Aware Denoising Loss is designed to enhance foreground reconstruction supervision. Experiments on various datasets show our method outperforms previous methods in overall camouflaged image quality and foreground fidelity.
View on arXiv@article{chen2025_2504.02180,
title={ Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation },
author={ Pei-Chi Chen and Yi Yao and Chan-Feng Hsu and HongXia Xie and Hung-Jen Chen and Hong-Han Shuai and Wen-Huang Cheng },
journal={arXiv preprint arXiv:2504.02180},
year={ 2025 }
}