Rethinking Camouflage Image Generation towards a Training-Free Paradigm
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...
| Authors | Haodong Yang, Zhongling Huang, Gong Cheng |
|---|---|
| Primary category | cs.CV |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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