4D Point Splatting for mmWave Radar Micro-Doppler Differentiable Rendering

Millimeter-wave radar resolves the micrometer displacements of a moving body as micro-Doppler, enabling gait, gesture, activity, pose and vital-sign sensing. Each task relies on a downstream model trained on real data, yet radar datasets lag orders of magnitude behind vision and language. Brute-force collection will not close that gap, and existing synthesis methods (forward-only simulators, learned generators and static-scene inverse renderers) cannot fit a real capture of a moving body, leaving a substantial sim2real gap. We present 4D Point Splatting (4DPS), a physics-based differentiable renderer that fits an explicit moving scene to a real capture. Using the capture's synchronized camera to initialize the room and the moving body, we represent the scene as oriented points carrying material, normal and velocity, and splat each point into range and Doppler bins. Our output is the raw complex return a real radar records, so we can apply the same digital signal processing (DSP) on the rendered data, and obtain any DSP representation to run on and train any SOTA downstream model. We fit 235 sequences on HuPR and 100 on RT-Pose, two datasets with different radars. Our reconstructions set the state of the art, with median correlations of 0.940 and 0.830 on range–azimuth, 0.829 and 0.695 on range–Doppler, and 0.988 and 0.966 on micro-Doppler. Our renders also set the state of the art in downstream sim2real gap. On RT-Pose, a pose model trained on them alone reaches 21.9 cm MPJPE on real data, against 16.7 cm for real training and 127.6 cm for RF-Genesis baseline, and we show that both the rendered- and real-trained models learn nearly the same features. Finally, our custom CUDA kernels fit a sequence in 5.7 minutes on HuPR and 12.9 minutes on RT-Pose on one H100, making dataset-scale inverse rendering of mmWave radar possible for the first time. Together, this work can enable synthetic data generation for any human-centered downstream task and model, and scene editing for data augmentation.