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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.
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