发表机构
The University of Tokyo; Mongolian University of Science and Technology; United Arab Emirates University（东京大学; 蒙古科技大学; 阿联酋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现实中洪水数据短缺及鱼眼图像失真致高精度洪水模拟难的问题，提出PhysFlood系统，利用扩散模型从单张鱼眼图像合成逼真洪水，可自由控制生成多样场景，实验证明其模拟图像有逼真度和鲁棒性。
AI 中文摘要
预测城市灾害行为的物理模拟，如与气候相关的洪水，在防灾和异常检测模型发展中起关键作用。然而，现实环境中洪水数据严重短缺，加上用于城市监控的鱼眼镜头图像存在固有失真，使得高精度模拟具有挑战性。为解决此问题，我们提出了新的物理模拟系统PhysFlood，它利用扩散模型从鱼眼镜头捕获的单张图像合成逼真的洪水。该系统不仅能从单张图像进行模拟，还能通过操纵水位等物理有意义的变量自由控制并生成多样的洪水场景。在评估实验中，我们进行了定性的人类研究，证明PhysFlood生成的模拟图像具有可接受的逼真度和鲁棒性。
英文摘要
Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and the development of anomaly detection models. However, the severe shortage of flood data in real-world environments, combined with the inherent distortions of fisheye lens images, which are used for urban surveillance, has made high-precision simulations challenging. To address this, we propose a new physical simulation system PhysFlood that leverages Diffusion Models to synthesize realistic floods from just a single image captured by a fisheye lens. Our system not only enables simulation from a single image, but also features the ability to freely control and generate diverse flood scenarios by manipulating physically meaningful variables, such as water levels. In our evaluation experiments, we conducted a qualitative human study and demonstrated that the simulation images generated by PhysFlood exhibit both acceptable realism and robustness.
