Scalable RF Simulation in Generative 4D Worlds
This work addresses the problem of data scarcity for RF sensing in indoor environments, offering a scalable simulation tool for researchers and practitioners in wireless sensing and ML.
The paper tackles the challenge of collecting high-quality RF data for indoor perception by introducing WaveVerse, a framework that simulates realistic RF signals from generated indoor scenes with human motions, enabling data generation for RF imaging for the first time and achieving performance gains in tasks like imaging and activity recognition.
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.