Deformable and Multi-view Gradient-Aligned Physical Adversarial Camouflage

Physical adversarial camouflage poses a significant threatto object detectors in safety-critical applications. However, synthesiz-ing robust 3D textures remains non-trivial due to the structural mis-match between static texture parameters and dynamic physical obser-vations. In this paper, we identify two fundamental optimization hur-dles: spatially inconsistent gradients arising from sparse rendering vis-ibility; and cross-view gradient misalignment, where contradictory sig-nals from diverse viewpoints cause destructive interference. To overcomethese, we propose a unified framework that reconciles spatial continu-ity with optimization stability. First, the Deformable Texture Field pa-rameterizes textures as continuous fields via learnable flow grids, en-forcing intrinsic smoothness while enabling adaptive geometric defor-mation to enhance the texture’s representational flexibility. Second, aGradient-Aligned Meta-Optimization strategy leverages multi-step ex-ploration trajectories to implicitly maximize gradient alignment acrossconflicting viewpoints. Extensive digital and physical experiments demon-strate that our method achieves state-of-the-art robustness and transfer-ability against advanced detectors under diverse viewing conditions.