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

arXiv: https://arxiv.org/abs/2108.00249 · HTML: https://mlsanity.com/paper/2108.00249/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 31 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"](https://mlsanity.com/paper/1908.07201/) (2019)
- [Creatures great and SMAL: Recovering the shape and motion of animals from video](https://mlsanity.com/paper/1811.05804/) (2018)
- [Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop](https://mlsanity.com/paper/2007.11110/) (2020)
- [Learning from Synthetic Animals](https://mlsanity.com/paper/1912.08265/) (2019)
- [Synthesizing Training Images for Boosting Human 3D Pose Estimation](https://mlsanity.com/paper/1604.02703/) (2016)
- [RGBD-Dog: Predicting Canine Pose from RGBD Sensors](https://mlsanity.com/paper/2004.07788/) (2020)
- [Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification](https://mlsanity.com/paper/1701.03153/) (2017)
- [Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection](https://mlsanity.com/paper/1807.01990/) (2018)

## Built on by (most cited first, 9 of 9)

- [Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding](https://mlsanity.com/paper/2204.08129/) (2022)
- [MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding](https://mlsanity.com/paper/2306.00576/) (2023)
- [ScarceNet: Animal Pose Estimation with Scarce Annotations](https://mlsanity.com/paper/2303.15023/) (2023)
- [UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning](https://mlsanity.com/paper/2308.09953/) (2023)
- [CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx](https://mlsanity.com/paper/2506.04931/) (2025)
- [UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation](https://mlsanity.com/paper/2507.02713/) (2025)
- [Advances and Trends in the 3D Reconstruction of the Shape and Motion of Animals](https://mlsanity.com/paper/2508.16062/) (2025)
- [Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior](https://mlsanity.com/paper/2307.13361/) (2023)
- [AP-CAP: Advancing High-Quality Data Synthesis for Animal Pose Estimation via a Controllable Image Generation Pipeline](https://mlsanity.com/paper/2504.00394/) (2025)

## Abstract (arXiv)

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.