Physics-Grounded Monocular Video-to-Radar Synthesis for Cross-Modal Data Augmentation

Abstract

Radar-based human sensing has emerged as a robust modality for activity monitoring and privacy-preserving healthcare. However, its development is constrained by the limited availability of diverse and annotated radar datasets. In this letter, we propose a physics-grounded cross-modal framework that synthesizes radar echo signals directly from monocular video. By integrating data-driven pose estimation with physics-constrained scale recovery, the proposed method reconstructs metric-consistent human motion in 3-D space. Furthermore, a surface-consistent scattering model based on a flattened ellipsoid representation is introduced to capture realistic electromagnetic reflections under millimeter-wave conditions. Experimental results show that the synthesized radar spectrograms reproduce motion-dependent Doppler characteristics and achieve over 82% structural similarity with real measurements. The proposed approach provides a low-cost pathway for radar data augmentation by transforming vision data into radar signals.