SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation
This addresses the problem of limited training data for animal pose estimation, benefiting researchers in fields like biomechanics and robotics, though it is incremental as it builds on existing synthetic data methods.
The authors tackled the lack of animal pose datasets by introducing SyDog, a synthetic dog dataset generated with Unity, and showed that models trained on it achieve better performance than those trained on real data, reducing the need for labor-intensive labeling.
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.