Location-Free Camouflage Generation Network

• URL: http://arxiv.org/abs/2203.09845v1
• Date: Fri, 18 Mar 2022 10:33:40 GMT
• Title: Location-Free Camouflage Generation Network
• Authors: Yangyang Li, Wei Zhai, Yang Cao, Zheng-jun Zha
• Abstract summary: 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. 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. 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.
• Score: 82.74353843283407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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