  Color images are easy to understand visually and can acquire a great deal of
information, such as color and texture. They are highly and widely used in
tasks such as segmentation. On the other hand, in indoor person segmentation,
it is necessary to collect person data considering privacy. We propose a new
task for human segmentation from invisible information, especially airborne
ultrasound. We first convert ultrasound waves to reflected ultrasound
directional images (ultrasound images) to perform segmentation from invisible
information. Although ultrasound images can roughly identify a person's
location, the detailed shape is ambiguous. To address this problem, we propose
a collaborative learning probabilistic U-Net that uses ultrasound and
segmentation images simultaneously during training, closing the probabilistic
distributions between ultrasound and segmentation images by comparing the
parameters of the latent spaces. In inference, only ultrasound images can be
used to obtain segmentation results. As a result of performance verification,
the proposed method could estimate human segmentations more accurately than
conventional probabilistic U-Net and other variational autoencoder models.
