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4D human perception plays an essential role in a myriad of applications, suchas home automation and metaverse avatar simulation. However, existing solutionswhich mainly rely on cameras and wearable devices are either privacy intrusiveor inconvenient to use. To address these issues, wireless sensing has emergedas a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signalsfor device-free human sensing. In this paper, we propose MM-Fi, the firstmulti-modal non-intrusive 4D human dataset with 27 daily or rehabilitationaction categories, to bridge the gap between wireless sensing and high-levelhuman perception tasks. MM-Fi consists of over 320k synchronized frames of fivemodalities from 40 human subjects. Various annotations are provided to supportpotential sensing tasks, e.g., human pose estimation and action recognition.Extensive experiments have been conducted to compare the sensing capacity ofeach or several modalities in terms of multiple tasks. We envision that MM-Fican contribute to wireless sensing research with respect to action recognition,human pose estimation, multi-modal learning, cross-modal supervision, andinterdisciplinary healthcare research.

MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing