  This paper aims to caption daily life --i.e., to create a textual description
of people's activities and interactions with objects in their homes. Addressing
this problem requires novel methods beyond traditional video captioning, as
most people would have privacy concerns about deploying cameras throughout
their homes. We introduce RF-Diary, a new model for captioning daily life by
analyzing the privacy-preserving radio signal in the home with the home's
floormap. RF-Diary can further observe and caption people's life through walls
and occlusions and in dark settings. In designing RF-Diary, we exploit the
ability of radio signals to capture people's 3D dynamics, and use the floormap
to help the model learn people's interactions with objects. We also use a
multi-modal feature alignment training scheme that leverages existing
video-based captioning datasets to improve the performance of our radio-based
captioning model. Extensive experimental results demonstrate that RF-Diary
generates accurate captions under visible conditions. It also sustains its good
performance in dark or occluded settings, where video-based captioning
approaches fail to generate meaningful captions. For more information, please
visit our project webpage: http://rf-diary.csail.mit.edu
