{"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/panoptop-a-framework-for-generating-viewpoint","title":"PanopTOP: a framework for generating viewpoint-invariant human pose estimation datasets","arxiv_id":null,"date":"2021-10-11","proceeding":"ICCV 2021 2021 10","authors":["Nicola Garau","Giulia Martinelli","Piotr Bròdka","Niccolò Bisagno","Nicola Conci"],"abstract":"Human pose estimation (HPE) from RGB and depth images has recently experienced a push for viewpoint-invariant and scale-invariant pose retrieval methods. Current methods fail to generalize to unconventional viewpoints due to the lack of viewpoint-invariant data at training time. Existing datasets do not provide multiple-viewpoint observations and mostly focus on frontal views. In this work, we introduce PanopTOP, a fully automatic framework for the generation of semi-synthetic RGB and depth samples with 2D and 3D ground truth of pedestrian poses from multiple arbitrary viewpoints. Starting from the Panoptic Dataset [15], we use the PanopTOP framework to generate the PanopTOP31K dataset, consisting of 31K images from 23 different subjects recorded from diverse and challenging viewpoints, also including the top-view. Finally, we provide baseline results and cross-validation tests for our dataset, demonstrating how it is possible to generalize from the semi-synthetic to the real-world domain. The dataset and the code will be made publicly available upon acceptance.","url_abs":"https://openaccess.thecvf.com/content/ICCV2021W/DSC/html/Garau_PanopTOP_A_Framework_for_Generating_Viewpoint-Invariant_Human_Pose_Estimation_Datasets_ICCVW_2021_paper.html","url_pdf":"https://openaccess.thecvf.com/content/ICCV2021W/DSC/papers/Garau_PanopTOP_A_Framework_for_Generating_Viewpoint-Invariant_Human_Pose_Estimation_Datasets_ICCVW_2021_paper.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":[{"paper_slug":"panoptop-a-framework-for-generating-viewpoint","repo_url":"https://github.com/mmlab-cv/PanopTOP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"pose-retrieval","task_name":"Pose Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[{"slug":"panoptop31k","name":"PanopTOP31K","full_name":"PanopTOP31K"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}