# Differentiable Radar Rendering

Human micro-Doppler from millimeter-wave radar has lacked a scalable data source: forward-only simulators, learned generators that hide the scene in network weights, and static-scene inverse renderers with no slow-time axis all fail to fit a real capture of a moving body. 4DPS represents the scene as oriented points carrying material, normal and velocity, and splats each point into range and Doppler bins through a closed-form Doppler point spread function, emitting the raw complex return a real radar records. On HuPR and RT-Pose the fitted renders reach median micro-Doppler correlations of 0.988 and 0.966, and pose models trained on renders alone keep 0.59 and 0.76 of their real-trained accuracy on real data.

Human micro-Doppler from millimeter-wave radar has lacked a scalable data source: forward-only simulators, learned generators that hide the scene in network weights, and static-scene inverse renderers with no slow-time axis all fail to fit a real capture of a moving body. 4DPS represents the scene as oriented points carrying material, normal and velocity, and splats each point into range and Doppler bins through a closed-form Doppler point spread function, emitting the raw complex return a real radar records. On HuPR and RT-Pose the fitted renders reach median micro-Doppler correlations of 0.988 and 0.966, and pose models trained on renders alone keep 0.59 and 0.76 of their real-trained accuracy on real data.