Flexible Physical Camouflage Generation Based on a Differential Approach

arXiv:2402.13575v1 Announce Type: new
Abstract: This study introduces a novel approach to neural rendering, specifically tailored for adversarial camouflage, within an extensive 3D rendering framework. Our method, named FPA, goes beyond traditional techniques by faithfully simulating lighting conditions and material variations, ensuring a nuanced and realistic representation of textures on a 3D target. To achieve this, we employ a generative approach that learns adversarial patterns from a diffusion model. This involves incorporating a specially designed adversarial loss and covert constraint …

3d rendering abstract adversarial arxiv beyond cs.ai cs.cv differential framework lighting material neural rendering novel rendering representation study type