  WiFi-based human sensing has exhibited remarkable potential to analyze user
behaviors in a non-intrusive and device-free manner, benefiting applications as
diverse as smart homes and healthcare. However, most previous works focus on
single-user sensing, which has limited practicability in scenarios involving
multiple users. Although recent studies have begun to investigate WiFi-based
multi-user sensing, there remains a lack of benchmark datasets to facilitate
reproducible and comparable research. To bridge this gap, we present WiMANS, to
our knowledge, the first dataset for multi-user sensing based on WiFi. WiMANS
contains over 9.4 hours of dual-band WiFi Channel State Information (CSI), as
well as synchronized videos, monitoring simultaneous activities of multiple
users. We exploit WiMANS to benchmark the performance of state-of-the-art
WiFi-based human sensing models and video-based models, posing new challenges
and opportunities for future work. We believe WiMANS can push the boundaries of
current studies and catalyze the research on WiFi-based multi-user sensing.
