Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and
  Synthetic Prior
Jose Sosa Sharn Perry Jane Alty David Hogg
Numerous fields, such as ecology, biology, and neuroscience, use animal
recordings to track and measure animal behaviour. Over time, a significant
volume of such data has been produced, but some computer vision techniques
cannot explore it due to the lack of annotations. To address this, we propose
an approach for estimating 2D mouse body pose from unlabelled images using a
synthetically generated empirical pose prior. Our proposal is based on a recent
self-supervised method for estimating 2D human pose that uses single images and
a set of unpaired typical 2D poses within a GAN framework. We adapt this method
to the limb structure of the mouse and generate the empirical prior of 2D poses
from a synthetic 3D mouse model, thereby avoiding manual annotation. In
experiments on a new mouse video dataset, we evaluate the performance of the
approach by comparing pose predictions to a manually obtained ground truth. We
also compare predictions with those from a supervised state-of-the-art method
for animal pose estimation. The latter evaluation indicates promising results
despite the lack of paired training data. Finally, qualitative results using a
dataset of horse images show the potential of the setting to adapt to other
animal species.