This paper presents AnthropoCam, a mobile-based neural style transfer (NST) system optimized for the visual synthesis of Anthropocene environments. Unlike conventional artistic NST, which prioritizes painterly abstraction, stylizing human-altered landscapes demands a careful balance between amplifying material textures and preserving semantic legibility. Industrial infrastructures, waste accumulations, and modified ecosystems contain dense, repetitive patterns that are visually expressive yet highly susceptible to semantic erosion under aggressive style transfer. To address this challenge, we systematically investigate the impact of NST parameter configurations on the visual translation of Anthropocene textures, including feature layer selection, style and content loss weighting, training stability, and output resolution. Through controlled experiments, we identify an optimal parameter manifold that maximizes stylistic expression while preventing semantic erasure. Our results demonstrate that appropriate combinations of convolutional depth, loss ratios, and resolution scaling enable the faithful transformation of anthropogenic material properties into a coherent visual language. Building on these findings, we implement a low-latency, feed-forward NST pipeline deployed on mobile devices. The system integrates a React Native frontend with a Flask-based GPU backend, achieving high-resolution inference within 3-5 seconds on general mobile hardware. This enables real-time, in-situ visual intervention at the site of image capture, supporting participatory engagement with Anthropocene landscapes. By coupling domain-specific NST optimization with mobile deployment, AnthropoCam reframes neural style transfer as a practical and expressive tool for real-time environmental visualization in the Anthropocene.

翻译：本文提出AnthropoCam，一种专为Anthropocene环境视觉合成优化的移动端神经风格迁移（NST）系统。与优先考虑绘画抽象的传统艺术性NST不同，对人类改造景观的风格化需要在增强材质纹理与保持语义可读性之间取得精细平衡。工业基础设施、废弃物堆积及改造后的生态系统包含密集且重复的图案，这些图案具有视觉表现力，但在激进的风格迁移下极易发生语义侵蚀。为应对这一挑战，我们系统研究了NST参数配置对Anthropocene纹理视觉转换的影响，包括特征层选择、风格与内容损失权重、训练稳定性及输出分辨率。通过受控实验，我们确定了能最大化风格表达同时防止语义擦除的最优参数流形。实验结果表明，卷积深度、损失比率与分辨率缩放的适当组合，能够将人为改造的材质属性忠实转化为连贯的视觉语言。基于这些发现，我们实现了部署于移动设备的低延迟前馈式NST流水线。该系统将React Native前端与基于Flask的GPU后端集成，在通用移动硬件上实现3-5秒内完成高分辨率推理。这使得在图像采集现场能够进行实时原位视觉干预，支持对Anthropocene景观的参与式观察。通过将领域特定的NST优化与移动端部署相结合，AnthropoCam将神经风格迁移重塑为Anthropocene时代实时环境可视化的一种实用且富有表现力的工具。