{"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/action-agnostic-human-pose-forecasting","title":"Action-Agnostic Human Pose Forecasting","arxiv_id":"1810.09676","date":"2018-10-23","proceeding":null,"authors":["Hsu-kuang Chiu","Ehsan Adeli","Borui Wang","De-An Huang","Juan Carlos Niebles"],"abstract":"Predicting and forecasting human dynamics is a very interesting but\nchallenging task with several prospective applications in robotics,\nhealth-care, etc. Recently, several methods have been developed for human pose\nforecasting; however, they often introduce a number of limitations in their\nsettings. For instance, previous work either focused only on short-term or\nlong-term predictions, while sacrificing one or the other. Furthermore, they\nincluded the activity labels as part of the training process, and require them\nat testing time. These limitations confine the usage of pose forecasting models\nfor real-world applications, as often there are no activity-related annotations\nfor testing scenarios. In this paper, we propose a new action-agnostic method\nfor short- and long-term human pose forecasting. To this end, we propose a new\nrecurrent neural network for modeling the hierarchical and multi-scale\ncharacteristics of the human dynamics, denoted by triangular-prism RNN\n(TP-RNN). Our model captures the latent hierarchical structure embedded in\ntemporal human pose sequences by encoding the temporal dependencies with\ndifferent time-scales. For evaluation, we run an extensive set of experiments\non Human 3.6M and Penn Action datasets and show that our method outperforms\nbaseline and state-of-the-art methods quantitatively and qualitatively. Codes\nare available at https://github.com/eddyhkchiu/pose_forecast_wacv/","url_abs":"http://arxiv.org/abs/1810.09676v1","url_pdf":"http://arxiv.org/pdf/1810.09676v1.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":"action-agnostic-human-pose-forecasting","repo_url":"https://github.com/eddyhkchiu/pose_forecast_wacv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"human-dynamics","task_name":"Human Dynamics"},{"task_slug":"human-pose-forecasting","task_name":"Human Pose Forecasting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/human-pose-forecasting-on-human36m","task":"Human Pose Forecasting","dataset":"Human3.6M","model":"TP-RNN","rank_in_archive_order":19,"of":33,"metrics":{"MAR, walking, 1,000ms":"0.77","MAR, walking, 400ms":"0.65"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1810.09676","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}