  Liquid state estimation is important for robotics tasks such as pouring;
however, estimating the state of transparent liquids is a challenging problem.
We propose a novel segmentation pipeline that can segment transparent liquids
such as water from a static, RGB image without requiring any manual annotations
or heating of the liquid for training. Instead, we use a generative model that
is capable of translating images of colored liquids into synthetically
generated transparent liquid images, trained only on an unpaired dataset of
colored and transparent liquid images. Segmentation labels of colored liquids
are obtained automatically using background subtraction. Our experiments show
that we are able to accurately predict a segmentation mask for transparent
liquids without requiring any manual annotations. We demonstrate the utility of
transparent liquid segmentation in a robotic pouring task that controls pouring
by perceiving the liquid height in a transparent cup. Accompanying video and
supplementary materials can be found
