发表机构
Xiamen University Malaysia; The Hong Kong University of Science and Technology; University of North Carolina at Charlotte（厦门大学马来西亚分校; 香港科技大学; 北卡罗来纳大学夏洛特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对毫米波雷达信号模拟成本高问题，提出HybridSim混合模拟器，用三平面和图卷积网络等提取人体特征、稳定优化，通过逆渲染等建模信号路径，实验显示其能有效增强特定地点数据，提升人体感知任务表现。
AI 中文摘要
毫米波雷达信号对动态人体运动的高保真模拟，对于开发基于雷达的人体感知模型很有价值；但为特定部署地点收集精确标记的测量数据仍然成本高昂。我们提出了HybridSim，这是一种物理学习混合模拟器，它能在固定室内房间配置下，从动态人体网格合成毫米波雷达信号，将传播明确解耦为两个分量。为参数化人体对象，我们使用三平面表示提取人体特征，并使用图卷积网络稳定优化并减轻梯度不稳定性。直接信号路径通过具有微面元双向反射分布函数的逆渲染公式建模，以捕获主要表面反射。同时，间接路径通过将3D高斯点云与虚拟接收器几何相结合来近似，以拟合和再现特定地点的多径干扰模式，计算成本远低于显式全光线追踪。在固定房间设置中的实验表明，当使用HybridSim进行特定地点数据增强时，与基于物理的参考有更好的一致性，并且在下游基于雷达的人体感知任务上有持续的增益。
英文摘要
High-fidelity simulation of mmWave radar signals for dynamic human motion is valuable for developing radar-based human sensing models; yet collecting accurately labeled measurements for a specific deployment site remains expensive. We present HybridSim, a physics-learning hybrid simulator that synthesizes mmWave radar signals from dynamic human meshes under a fixed indoor room configuration, explicitly decoupling propagation into two components. To parameterize the human subject, we use a tri-plane representation to extract human features and a Graph Convolutional Network to stabilize optimization and mitigate gradient instability. The direct signal path is modeled via an inverse-rendering formulation with a microfacet BRDF to capture primary surface reflections. In parallel, the indirect path is approximated by combining 3D Gaussian Splatting with a virtual-receiver geometry to fit and reproduce site-specific multipath interference patterns, achieving substantially lower computational cost than explicit full ray tracing. Experiments in a fixed-room setting show improved agreement with a physically based reference and consistent gains on downstream radar-based human sensing tasks when using HybridSim for site-specific data augmentation.
CommentsAccepted to ECCV 2026. Project Page: https://weitao-xiong.github.io/HybridSim/
