  Millimeter wave radar is gaining traction recently as a promising modality
for enabling pervasive and privacy-preserving gesture recognition. However, the
lack of rich and fine-grained radar datasets hinders progress in developing
generalized deep learning models for gesture recognition across various user
postures (e.g., standing, sitting), positions, and scenes. To remedy this, we
resort to designing a software pipeline that exploits wealthy 2D videos to
generate realistic radar data, but it needs to address the challenge of
simulating diversified and fine-grained reflection properties of user gestures.
To this end, we design G3R with three key components: (i) a gesture reflection
point generator expands the arm's skeleton points to form human reflection
points; (ii) a signal simulation model simulates the multipath reflection and
attenuation of radar signals to output the human intensity map; (iii) an
encoder-decoder model combines a sampling module and a fitting module to
address the differences in number and distribution of points between generated
and real-world radar data for generating realistic radar data. We implement and
evaluate G3R using 2D videos from public data sources and self-collected
real-world radar data, demonstrating its superiority over other
state-of-the-art approaches for gesture recognition.
