SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation

TL;DR

SyDog is a synthetic dog dataset created using Unity that improves 2D dog pose estimation by providing abundant labeled data, reducing labeling effort, and enhancing model performance.

Contribution

The paper introduces SyDog, a novel synthetic dataset for dog pose estimation, addressing data scarcity and improving model accuracy over real-data-only training.

Findings

Models trained on SyDog outperform those trained on real data.

SyDog significantly reduces manual labeling effort.

The dataset serves as a benchmark for animal motion research.

Abstract

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.