HybridSim: A Physics–Learning Hybrid Digital Twin for mmWave Human Sensing

High-fidelity simulation of mmWave radar signals for dynamichuman motion is valuable for developing radar-based human sensing mod-els; yet collecting accurately labeled measurements for a specific deploy-ment site remains expensive. We present HybridSim, a physics–learninghybrid simulator that synthesizes mmWave radar signals from dynamichuman meshes under a fixed indoor room configuration, explicitly de-coupling propagation into two components. To parameterize the humansubject, we use a tri-plane representation to extract human features anda Graph Convolutional Network to stabilize optimization and mitigategradient instability. The direct signal path is modeled via an inverse-rendering formulation with a microfacet BRDF to capture primary surfacereflections. In parallel, the indirect path is approximated by combining3D Gaussian Splatting with a virtual-receiver geometry to fit and repro-duce site-specific multipath interference patterns, achieving substantiallylower computational cost than explicit full ray tracing. Experiments ina fixed-room setting show improved agreement with a physically basedreference and consistent gains on downstream radar-based human sensingtasks when using HybridSim for site-specific data augmentation.