mlsanityData to 7 Oct 2026

GANmouflage: 3D Object Nondetection with Texture Fields

arXiv 2201.07202 ↗First posted 18 Jan 2022cs.CVcited by 7

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Abstract (arXiv)

We propose a method that learns to camouflage 3D objects within scenes. Given an object's shape and a distribution of viewpoints from which it will be seen, we estimate a texture that will make it difficult to detect. Successfully solving this task requires a model that can accurately reproduce textures from the scene, while simultaneously dealing with the highly conflicting constraints imposed by each viewpoint. We address these challenges with a model based on texture fields and adversarial learning. Our model learns to camouflage a variety of object shapes from randomly sampled locations and viewpoints within the input scene, and is the first to address the problem of hiding complex object shapes. Using a human visual search study, we find that our estimated textures conceal objects significantly better than previous methods. Project site: https://rrrrrguo.github.io/ganmouflage/

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 64 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

30 of 64 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 0; 2026: 1.

7 papers.

  1. 2023Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Modelscited by 46
  2. 2024Factorized Diffusion: Perceptual Illusions by Noise Decompositioncited by 23
  3. 2023The Making and Breaking of Camouflagecited by 14
  4. 2022Volumetric Disentanglement for 3D Scene Manipulationcited by 13
  5. 2024Illusion3D: 3D Multiview Illusion with 2D Diffusion Priorscited by 5
  6. 2023Coevolution of Camouflagecited by 0
  7. 2026Mirror Illusion Artcited by 0