4D human perception plays an essential role in a myriad of applications, such as home automation and metaverse avatar simulation. However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use. To address these issues, wireless sensing has emerged as a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signals for device-free human sensing. In this paper, we propose MM-Fi, the first multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation action categories, to bridge the gap between wireless sensing and high-level human perception tasks. MM-Fi consists of over 320k synchronized frames of five modalities from 40 human subjects. Various annotations are provided to support potential sensing tasks, e.g., human pose estimation and action recognition. Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks. We envision that MM-Fi can contribute to wireless sensing research with respect to action recognition, human pose estimation, multi-modal learning, cross-modal supervision, and interdisciplinary healthcare research.

翻译：4D人体感知在家庭自动化和元宇宙化身模拟等多种应用中扮演着关键角色。然而，现有主要依赖摄像头和可穿戴设备的解决方案要么侵犯隐私，要么使用不便。为解决这些问题，无线感知作为一种有前景的替代方案应运而生，利用LiDAR、毫米波雷达和WiFi信号实现无设备人体感知。本文提出MM-Fi——首个包含27种日常或康复动作类别的多模态非侵入式4D人体数据集，旨在弥合无线感知与高级人体感知任务之间的差距。MM-Fi包含来自40名受试者的超过32万帧同步多模态数据，涵盖五种模态。为支持潜在感知任务（如人体姿态估计和行为识别）提供了多种标注。我们开展了大量实验，比较了每种或多种模态在不同任务中的感知能力。我们预期MM-Fi将推动无线感知研究在行为识别、人体姿态估计、多模态学习、跨模态监督以及跨学科医疗健康研究方面的发展。