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\title{CamoX: A Diffusion-based Method with Few-shot Learning for
Environment-guided Camouflage Pattern Generation}



\author[1]{Tran Thanh Phong Nguyen}%
\author[1]{Tauseef Gulrez}%
\author[1]{Joanne B Culpepper}%
\author[1]{Hoang Thanh Le}%
\author[1]{Son Lam Phung}%
\affil[1]{Affiliation not available}%


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  \date{April 25, 2026}


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\begin{abstract}
Effective camouflage is needed for defense personnel and assets to blend
seamlessly with the complex and dynamic environments. The camouflage
technique must maintain the concealment capability across various
environments and geographic regions. However, existing approaches,
including manual design, computer-aided techniques, and deep learning
methods, face significant challenges in achieving automation,
scalability, and generalization across diverse and uncalibrated scenes.
To address these limitations, we propose a novel diffusion-based method
with few-shot learning to generate environment-guided camouflage
patterns. Our method, called CamoX, consists of two major stages: meta
learning and few-shot learning. In the meta learning stage, our method
introduces a latent diffusion-based architecture that automatically
generates camouflage patterns from noise, eliminating manual
intervention and enabling scalable production. In the few-shot learning
stage, our approach enforces similarity between the latent features of
the camouflage patterns and target scenes by optimizing the guided mean
absolute error loss. This innovation allows the generated camouflage
patterns to adapt seamlessly to multiple environments with minimal
retraining. Furthermore, this paper introduces a comprehensive
camouflage dataset, called Camo-Meta, comprising 144,750 realistic
camouflage patterns and associated metadata to support research in
camouflage generation. Experimental results on multiple datasets
demonstrate that CamoX outperforms existing state-of-the-art methods in
key metrics.%
\end{abstract}%



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