{"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/on-human-motion-prediction-using-recurrent","title":"On human motion prediction using recurrent neural networks","arxiv_id":"1705.02445","date":"2017-05-06","proceeding":"CVPR 2017 7","authors":["Julieta Martinez","Michael J. Black","Javier Romero"],"abstract":"Human motion modelling is a classical problem at the intersection of graphics\nand computer vision, with applications spanning human-computer interaction,\nmotion synthesis, and motion prediction for virtual and augmented reality.\nFollowing the success of deep learning methods in several computer vision\ntasks, recent work has focused on using deep recurrent neural networks (RNNs)\nto model human motion, with the goal of learning time-dependent representations\nthat perform tasks such as short-term motion prediction and long-term human\nmotion synthesis. We examine recent work, with a focus on the evaluation\nmethodologies commonly used in the literature, and show that, surprisingly,\nstate-of-the-art performance can be achieved by a simple baseline that does not\nattempt to model motion at all. We investigate this result, and analyze recent\nRNN methods by looking at the architectures, loss functions, and training\nprocedures used in state-of-the-art approaches. We propose three changes to the\nstandard RNN models typically used for human motion, which result in a simple\nand scalable RNN architecture that obtains state-of-the-art performance on\nhuman motion prediction.","url_abs":"http://arxiv.org/abs/1705.02445v1","url_pdf":"http://arxiv.org/pdf/1705.02445v1.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":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/una-dinosauria/human-motion-prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/YQRickWang/Motion_Prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/YQRickWang/tf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/enriccorona/human-motion-prediction-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/facebookresearch/QuaterNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/garroud/human-motion-prediction-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"on-human-motion-prediction-using-recurrent","repo_url":"https://github.com/nageshpindi/human-motion-prediction-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"human-pose-forecasting","task_name":"Human Pose Forecasting"},{"task_slug":"human-motion-prediction","task_name":"Human motion prediction"},{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"motion-synthesis","task_name":"Motion Synthesis"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.02445","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.02445"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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