Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals

TL;DR

This paper introduces a novel neural network that synthesizes human silhouette and skeleton videos solely from Wi-Fi signals, enabling visual data generation without cameras through cross-modality learning.

Contribution

It proposes a two-branch generative model with a teacher-student design that maps Wi-Fi radio data to visual features, replacing visual data with Wi-Fi signals.

Findings

Effective synthesis of silhouette videos from Wi-Fi signals

High-quality skeleton video generation demonstrated

Cross-modality supervision improves visual feature inference

Abstract

The increasing availability of wireless access points (APs) is leading towards human sensing applications based on Wi-Fi signals as support or alternative tools to the widespread visual sensors, where the signals enable to address well-known vision-related problems such as illumination changes or occlusions. Indeed, using image synthesis techniques to translate radio frequencies to the visible spectrum can become essential to obtain otherwise unavailable visual data. This domain-to-domain translation is feasible because both objects and people affect electromagnetic waves, causing radio and optical frequencies variations. In literature, models capable of inferring radio-to-visual features mappings have gained momentum in the last few years since frequency changes can be observed in the radio domain through the channel state information (CSI) of Wi-Fi APs, enabling signal-based feature extraction, e.g., amplitude. On this account, this paper presents a novel two-branch generative neural network that effectively maps radio data into visual features, following a teacher-student design that exploits a cross-modality supervision strategy. The latter conditions signal-based features in the visual domain to completely replace visual data. Once trained, the proposed method synthesizes human silhouette and skeleton videos using exclusively Wi-Fi signals. The approach is evaluated on publicly available data, where it obtains remarkable results for both silhouette and skeleton videos generation, demonstrating the effectiveness of the proposed cross-modality supervision strategy.