  Estimating the pose of animals can facilitate the understanding of animal
motion which is fundamental in disciplines such as biomechanics, neuroscience,
ethology, robotics and the entertainment industry. Human pose estimation models
have achieved high performance due to the huge amount of training data
available. Achieving the same results for animal pose estimation is challenging
due to the lack of animal pose datasets. To address this problem we introduce
SyDog: a synthetic dataset of dogs containing ground truth pose and bounding
box coordinates which was generated using the game engine, Unity. We
demonstrate that pose estimation models trained on SyDog achieve better
performance than models trained purely on real data and significantly reduce
the need for the labour intensive labelling of images. We release the SyDog
dataset as a training and evaluation benchmark for research in animal motion.
