3 papers
cs.CV2026
mmSimPrior: Learning Simulation Priors for Data-Efficient and Generalizable Real-World Radar-based Human Motion Reconstruction
Cheng Guo, Qiming Cao, Shengkai Xu +4
Millimeter-wave (mmWave) radar enables privacy-preserving and illumination-robust human motion reconstruction, but training generalizable models typically requires costly paired ra…
cs.CV2026
Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment
Yitong Shen, Cheng Guo, Peiliang Wang +5
Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi samples for every target cl…
cs.CV2026
HybridSim: A Physics-Learning Hybrid Digital Twin for mmWave Human Sensing
Weitao Xiong, Tianyu Liu, Peng Li +4
High-fidelity simulation of mmWave radar signals for dynamic human motion is valuable for developing radar-based human sensing models; yet collecting accurately labeled measurement…