mmSimPrior: Learning Simulation Priors for Data-Efficient Real-World Generalizable Radar-Based Human Motion Reconstruction 文章

arXiv:2607.22973v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar offers privacy-preserving and lighting-robust sensing for human motion reconstruction, but learning models that generalize across real deployments require diverse paired radar-motion data that are costly to collect. Simulation provides scalable supervision, yet models trained on clean synthetic signals transfer poorly because of multipath, clutter, response statistics, and resolution degradation. We present mmSimPrior, a simulation-pretrained framework that factorizes transferable knowledge into signal, motion, and radar-to-motion mapping priors. A multi-modal signal encoder is pretrained with a physics-informed domain-randomization curriculum that emulates propagation- and acquisition-level variations, while a joint-temporal tokenizer learns a discrete prior over plausible human motion.