  Camouflage is a common visual phenomenon, which refers to hiding the
foreground objects into the background images, making them briefly invisible to
the human eye. Previous work has typically been implemented by an iterative
optimization process. However, these methods struggle in 1) efficiently
generating camouflage images using foreground and background with arbitrary
structure; 2) camouflaging foreground objects to regions with multiple
appearances (e.g. the junction of the vegetation and the mountains), which
limit their practical application. To address these problems, this paper
proposes a novel Location-free Camouflage Generation Network (LCG-Net) that
fuse high-level features of foreground and background image, and generate
result by one inference. Specifically, a Position-aligned Structure Fusion
(PSF) module is devised to guide structure feature fusion based on the
point-to-point structure similarity of foreground and background, and introduce
local appearance features point-by-point. To retain the necessary identifiable
features, a new immerse loss is adopted under our pipeline, while a background
patch appearance loss is utilized to ensure that the hidden objects look
continuous and natural at regions with multiple appearances. Experiments show
that our method has results as satisfactory as state-of-the-art in the
single-appearance regions and are less likely to be completely invisible, but
far exceed the quality of the state-of-the-art in the multi-appearance regions.
Moreover, our method is hundreds of times faster than previous methods.
Benefitting from the unique advantages of our method, we provide some
downstream applications for camouflage generation, which show its potential.
The related code and dataset will be released at
https://github.com/Tale17/LCG-Net.
