Scalable RF Simulation in Generative 4D Worlds

TL;DR

WaveVerse is a scalable, prompt-based framework that simulates realistic RF signals in 4D indoor environments, enabling new applications in RF imaging and activity recognition.

Contribution

It introduces a novel language-guided 4D world generator and phase-coherent RF simulation, advancing RF data generation for indoor perception tasks.

Findings

Effective conditioned human motion generation

Improved RF imaging performance in data-limited scenarios

Demonstrated applications in activity recognition

Abstract

Radio Frequency (RF) sensing has emerged as a powerful, privacy-preserving alternative to vision-based methods for indoor perception tasks. However, collecting high-quality RF data in dynamic and diverse indoor environments remains a major challenge. To address this, we introduce WaveVerse, a prompt-based, scalable framework that simulates realistic RF signals from generated indoor scenes with human motions. WaveVerse introduces a language-guided 4D world generator, which includes a state-aware causal transformer for human motion generation conditioned on spatial constraints and texts, and a phase-coherent ray tracing simulator that enables the simulation of accurate and coherent RF signals. Experiments demonstrate the effectiveness of our approach in conditioned human motion generation and highlight how phase coherence is applied to beamforming and respiration monitoring. We further present two case studies in ML-based high-resolution imaging and human activity recognition, demonstrating that WaveVerse not only enables data generation for RF imaging for the first time, but also consistently achieves performance gain in both data-limited and data-adequate scenarios.