G3R: Generating Rich and Fine-grained mmWave Radar Data from 2D Videos for Generalized Gesture Recognition

TL;DR

This paper introduces G3R, a software pipeline that generates realistic, fine-grained radar data from 2D videos to improve gesture recognition across diverse postures and scenes.

Contribution

G3R is a novel framework that synthesizes detailed radar data from videos, addressing the scarcity of rich datasets for generalized gesture recognition.

Findings

G3R outperforms existing methods in gesture recognition accuracy.

Generated radar data closely matches real-world data in diversity and detail.

The approach enables robust recognition across various user postures and environments.

Abstract

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.