  Human silhouette segmentation, which is originally defined in computer
vision, has achieved promising results for understanding human activities.
However, the physical limitation makes existing systems based on optical
cameras suffer from severe performance degradation under low illumination,
smoke, and/or opaque obstruction conditions. To overcome such limitations, in
this paper, we propose to utilize the radio signals, which can traverse
obstacles and are unaffected by the lighting conditions to achieve silhouette
segmentation. The proposed RFMask framework is composed of three modules. It
first transforms RF signals captured by millimeter wave radar on two planes
into spatial domain and suppress interference with the signal processing
module. Then, it locates human reflections on RF frames and extract features
from surrounding signals with human detection module. Finally, the extracted
features from RF frames are aggregated with an attention based mask generation
module. To verify our proposed framework, we collect a dataset containing
804,760 radio frames and 402,380 camera frames with human activities under
various scenes. Experimental results show that the proposed framework can
achieve impressive human silhouette segmentation even under the challenging
scenarios(such as low light and occlusion scenarios) where traditional
optical-camera-based methods fail. To the best of our knowledge, this is the
first investigation towards segmenting human silhouette based on millimeter
wave signals. We hope that our work can serve as a baseline and inspire further
research that perform vision tasks with radio signals. The dataset and codes
will be made in public.
