  The assessment of laboratory animal behavior is of central interest in modern
neuroscience research. Behavior is typically studied in terms of pose changes,
which are ideally captured in three dimensions. This requires triangulation
over a multi-camera system which view the animal from different angles.
However, this is challenging in realistic laboratory setups due to occlusions
and other technical constrains. Here we propose the usage of lift-pose models
that allow for robust 3D pose estimation of freely moving rodents from a single
view camera view. To obtain high-quality training data for the pose-lifting, we
first perform geometric calibration in a camera setup involving bottom as well
as side views of the behaving animal. We then evaluate the performance of two
previously proposed model architectures under given inference perspectives and
conclude that reliable 3D pose inference can be obtained using temporal
convolutions. With this work we would like to contribute to a more robust and
diverse behavior tracking of freely moving rodents for a wide range of
experiments and setups in the neuroscience community.
