Camouflage design necessitates region-consistent colour and texture synthesis. However, practical datasets are frequently constrained by scarce paired supervision and cross-source distribution shift, which impede reliable generalisation across heterogeneous environments. To address this problem, we propose a region-feature-driven framework for camouflage generation from unpaired background sequences. The main framework-level contribution lies in constructing a decoupled sequence-conditioned regional representation and using it to drive a fused-background-first camouflage-generation pipeline under unpaired supervision. Specifically, each background sequence is encoded into a continuous region descriptor and combined with a discrete Environment × Season code; under unpaired supervision, this decoupled condition guides StyleGAN2-based fused-background synthesis via style modulation, after which a deterministic digital-camouflage renderer converts the synthesised fused background into a deployable pattern. On a stratified test split, the synthesised backgrounds achieve FID = 15.2 ± 0.5, KID = 4.7 ± 0.3 × 10⁻³, while also showing stable condition adherence under the tested settings. Auxiliary sequence-consistency proxy measures are PSNR = 34.56 dB, SSIM = 0.776, and LPIPS = 0.134. Under a paired forest-scenario detector protocol, the generated camouflage reduces mean image-best IoU from 0.716 to 0.400 for a one-stage detector and from 0.690 to 0.460 for a two-stage detector. Overall, these results support the effectiveness of the proposed condition design and transfer mechanism under the present experimental setting.