{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sydog-a-synthetic-dog-dataset-for-improved-2d","title":"SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation","arxiv_id":"2108.00249","date":"2021-07-31","proceeding":null,"authors":["Moira Shooter","Charles Malleson","Adrian Hilton"],"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.","url_abs":"https://arxiv.org/abs/2108.00249v1","url_pdf":"https://arxiv.org/pdf/2108.00249v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"2d-pose-estimation","task_name":"2D Pose Estimation"},{"task_slug":"animal-pose-estimation","task_name":"Animal Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[{"slug":"sydog","name":"SyDog","full_name":"A Synthetic Dog Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/animal-pose-estimation-on-stanfordextra","task":"Animal Pose Estimation","dataset":"StanfordExtra","model":"8 Stacked Hourglass Network","rank_in_archive_order":1,"of":3,"metrics":{"PCK@0.1":"78.65"},"uses_additional_data":false},{"leaderboard":"/sota/animal-pose-estimation-on-stanfordextra","task":"Animal Pose Estimation","dataset":"StanfordExtra","model":"2 Stacked Hourglass Network","rank_in_archive_order":2,"of":3,"metrics":{"PCK@0.1":"77.19"},"uses_additional_data":false},{"leaderboard":"/sota/animal-pose-estimation-on-stanfordextra","task":"Animal Pose Estimation","dataset":"StanfordExtra","model":"Mask R-CNN","rank_in_archive_order":3,"of":3,"metrics":{"PCK@0.1":"50.77"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.00249","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}