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The assessment of laboratory animal behavior is of central interest in modernneuroscience research. Behavior is typically studied in terms of pose changes,which are ideally captured in three dimensions. This requires triangulationover a multi-camera system which view the animal from different angles.However, this is challenging in realistic laboratory setups due to occlusionsand other technical constrains. Here we propose the usage of lift-pose modelsthat allow for robust 3D pose estimation of freely moving rodents from a singleview camera view. To obtain high-quality training data for the pose-lifting, wefirst perform geometric calibration in a camera setup involving bottom as wellas side views of the behaving animal. We then evaluate the performance of twopreviously proposed model architectures under given inference perspectives andconclude that reliable 3D pose inference can be obtained using temporalconvolutions. With this work we would like to contribute to a more robust anddiverse behavior tracking of freely moving rodents for a wide range ofexperiments and setups in the neuroscience community.

Evaluation of deep lift pose models for 3D rodent pose estimation based on geometrically triangulated data