Rethinking Camouflage Image Generation towards a Training-Free Paradigm

2026-09-13

AI Summary

本文提出一种无需训练的伪装图像生成方法FreeCam，通过冻结的修复扩散框架保留前景，并利用上下文推理模块和内在外观模块实现语义兼容性和外观同化。

Abstract

Camouflage image generation (CIG) aims to synthesize realistic camouflaged images by blending foreground objects into concealment-compatible background contexts. Achieving this objective requires jointly satisfying three coupled requirements: foreground preservation to retain target integrity, semantic compatibility to select plausible concealment contexts, and appearance assimilation to reduce visual discrepancies. Recent approaches predominantly rely on task-specific training on camouflage datasets to address these requirements, incurring substantial computational cost and limiting generalization beyond the training domain. To address these limitations, we formulate training-free CIG as a concealment-oriented paradigm that preserves the target while reducing its perceptual separability from the synthesized surroundings, rather than maintaining its visual prominence, without parameter updates. We instantiate this paradigm with FreeCam based on a frozen inpainting diffusion framework to preserve the foreground. Within this framework, a Contextual Reasoning Module exploits frozen multimodal priors to infer an environment favorable to concealment, thereby promoting semantic compatibility, while an Intrinsic Appearance Module extracts low-level color and texture cues from the foreground to guide background synthesis toward appearance assimilation. Extensive experiments demonstrate that FreeCam achieves state-of-the-art generation quality and camouflage effectiveness without task-specific training, while its generated images provide synthetic supervision for camouflaged object detection and reduce target detectability under general object detectors.

Problem

• Camouflage Image Generation
• Training-Free
• Foreground Preservation
• Semantic Compatibility
• Appearance Assimilation

Innovation

• Training-Free
• Foreground Preservation
• Semantic Compatibility
• Appearance Assimilation
• Inpainting Diffusion Framework