Rethinking Camouflage Image Generation towards a Training-Free Paradigm

• URL: http://arxiv.org/abs/2609.14377v1
• Date: Sun, 13 Sep 2026 08:24:45 GMT
• Title: Rethinking Camouflage Image Generation towards a Training-Free Paradigm
• Abstract summary: FreeCam generates realistic camouflaged images by blending foreground objects into concealment-compatible background contexts.<n>We formulate FreeCam as a concealment-oriented paradigm that preserves the target while reducing its perceptual separability from the synthesized surroundings.<n>Our experiments demonstrate that FreeCam achieves state-of-the-art generation quality and camouflage effectiveness without task-specific training.
• Score: 11.051625747450009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• To Blend In, First Decouple: Rethinking Camouflage Image Generation via Context-Decoupled Representations [18.9512030963437]
  Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds.<n>Existing methods typically follow either background-guided paradigms that adapt object appearance via style transfer, or foreground-guided strategies that outpaint surrounding regions conditioned on object features.<n>We propose a new context-decoupled generative paradigm, termed CamoDreamer, which aims to isolate contextual conditional guidance and explicitly decouple latent camouflage features into coordinated object and background control streams.
  arXiv  Detail & Related papers  (2026-07-20T10:01:36Z)
• LCPNet: Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection [50.3892176822492]
  Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds.<n>Deep learning methods have improved IRSTD by learning discriminative image-to-mask mappings.<n>Deep unfolding methods partially address this issue by embedding model-driven iterations into neural networks.<n>We propose Latent Consistent Proximal unfolding network (LCPNet) to address these limitations.
  arXiv  Detail & Related papers  (2026-07-06T02:15:09Z)
• CogSENet: Blind Image Deblurring with Blur-Conditioned Semantic Routing and Explicit Frequency Fusion [3.7521959864518593]
  CogSENet is a dynamic, semantic-aligned reconstruction framework inspired by the eagle's visual system.<n>CogSENetoutperforms state-of-the-art deblurring methods in both visual quality and structural fidelity with fewer parameters.
  arXiv  Detail & Related papers  (2026-06-29T09:29:21Z)
• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance [13.352489108641938]
  We propose a unified out-painting based framework for realistic camouflaged image generation.<n>ReamCamo explicitly introduces additional layout controls to regulate global image structure.<n>We also introduce a background-foreground distribution divergence metric that measures the effectiveness of camouflage in generated images.
  arXiv  Detail & Related papers  (2025-12-28T15:37:56Z)
• Text-guided Controllable Diffusion for Realistic Camouflage Images Generation [33.31050008276478]
  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.<n>We propose Controllable Text-guided Camouflage Images Generation method that produces realistic and logically plausible camouflage images.
  arXiv  Detail & Related papers  (2025-11-25T11:43:58Z)
• Consistent Human Image and Video Generation with Spatially Conditioned Diffusion [82.4097906779699]
  Consistent human-centric image and video synthesis aims to generate images with new poses while preserving appearance consistency with a given reference image.<n>We frame the task as a spatially-conditioned inpainting problem, where the target image is in-painted to maintain appearance consistency with the reference.<n>This approach enables the reference features to guide the generation of pose-compliant targets within a unified denoising network.
  arXiv  Detail & Related papers  (2024-12-19T05:02:30Z)
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
  Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out. Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
  arXiv  Detail & Related papers  (2023-10-31T06:11:23Z)
• Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection [47.653092957888596]
  We propose a framework for synthesizing camouflage data to enhance the detection of camouflaged objects in natural scenes.<n>Our approach employs a generative model to produce realistic camouflage images, which can be used to train existing object detection models.<n>Our framework outperforms the current state-of-the-art method on three datasets.
  arXiv  Detail & Related papers  (2023-08-13T06:55:05Z)
• CamDiff: Camouflage Image Augmentation via Diffusion Model [83.35960536063857]
  CamDiff is a novel approach to synthesize salient objects in camouflaged scenes. We leverage the latent diffusion model to synthesize salient objects in camouflaged scenes. Our approach enables flexible editing and efficient large-scale dataset generation at a low cost.
  arXiv  Detail & Related papers  (2023-04-11T19:37:47Z)
• Robust Region Feature Synthesizer for Zero-Shot Object Detection [87.79902339984142]
  We build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class Structure Preserving component. It is the first study to carry out zero-shot object detection in remote sensing imagery.
  arXiv  Detail & Related papers  (2022-01-01T03:09:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.