Text-guided Controllable Diffusion for Realistic Camouflage Images Generation

2025-11-25

AI Summary

Existing camouflaged image generation methods often neglect the semantic logical relationships between camouflaged objects and their backgrounds, resulting in unnatural fusion and implausible outpainting. To address this, we propose CT-CIG, a controllable text-guided camouflaged image generation framework. First, we introduce a Camouflage Revelation Dialogue Mechanism (CRDM) to generate highly relevant textual prompts. Second, we design a Frequency Interaction Refinement Module (FIRM) to enhance high-frequency texture modeling and scene-level integration. Third, leveraging a large vision-language model, we construct high-quality image-text pairs and fine-tune Stable Diffusion with a lightweight object localization controller. Experiments demonstrate that CT-CIG significantly outperforms state-of-the-art methods in both CLIPScore and camouflage effectiveness metrics, achieving simultaneous improvements in semantic consistency and visual realism.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Camouflage Images Generation (CIG) is an emerging research area that focuses on synthesizing images in which objects are harmoniously blended and exhibit high visual consistency with their surroundings. Existing methods perform CIG by either fusing objects into specific backgrounds or outpainting the surroundings via foreground object-guided diffusion. However, they often fail to obtain natural results because they overlook the logical relationship between camouflaged objects and background environments. To address this issue, we propose CT-CIG, a Controllable Text-guided Camouflage Images Generation method that produces realistic and logically plausible camouflage images. Leveraging Large Visual Language Models (VLM), we design a Camouflage-Revealing Dialogue Mechanism (CRDM) to annotate existing camouflage datasets with high-quality text prompts. Subsequently, the constructed image-prompt pairs are utilized to finetune Stable Diffusion, incorporating a lightweight controller to guide the location and shape of camouflaged objects for enhanced camouflage scene fitness. Moreover, we design a Frequency Interaction Refinement Module (FIRM) to capture high-frequency texture features, facilitating the learning of complex camouflage patterns. Extensive experiments, including CLIPScore evaluation and camouflage effectiveness assessment, demonstrate the semantic alignment of our generated text prompts and CT-CIG's ability to produce photorealistic camouflage images.

Problem

• Generating realistic camouflage images with logical object-background relationships
• Creating semantically aligned text prompts for camouflage image synthesis
• Enhancing camouflage pattern learning through high-frequency texture features

Innovation

• Leveraging VLM for high-quality text prompts
• Finetuning Stable Diffusion with lightweight controller
• Using frequency module to capture texture features