High-Resolution Gait Micro-Doppler Synthesis from Videos Over Diverse Trajectories

In recent years, there has been increasing interest in human body motion analysis, with applications in activity classification, human gait analysis, and human intent recognition. Among the various methods, millimeter-wave (mmWave)-based approaches have become popular due to their inherent contactless and privacy-preserving properties, as well as their resilience to lighting, weather conditions, and measurement distance. However, mmWave-based human motion analysis is still in its early stages, primarily due to the limited availability of large-scale datasets. This is particularly true for tasks that require high-resolution motion measurements. In this work, we introduce a novel method for synthesizing high-resolution mmWave datasets directly from videos. The proposed approach is well-suited for applications requiring very high-resolution Doppler signature simulations, such as analyzing subtle hand motions of walking pedestrians. We achieve this by employing an adversarial training strategy combined with custom task-specific loss functions that enhance the micro-motion signatures in the hands and legs of walking pedestrians. This work is the first to design and validate a high-resolution synthesized Doppler dataset of walking activities across multiple trajectories and subjects.