{"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/progressively-generating-better-initial","title":"Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction","arxiv_id":"2203.16051","date":"2022-03-30","proceeding":"CVPR 2022 1","authors":["Tiezheng Ma","Yongwei Nie","Chengjiang Long","Qing Zhang","Guiqing Li"],"abstract":"This paper presents a high-quality human motion prediction method that accurately predicts future human poses given observed ones. Our method is based on the observation that a good initial guess of the future poses is very helpful in improving the forecasting accuracy. This motivates us to propose a novel two-stage prediction framework, including an init-prediction network that just computes the good guess and then a formal-prediction network that predicts the target future poses based on the guess. More importantly, we extend this idea further and design a multi-stage prediction framework where each stage predicts initial guess for the next stage, which brings more performance gain. To fulfill the prediction task at each stage, we propose a network comprising Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph Convolutional Networks (T-DGCN). Alternatively executing the two networks helps extract spatiotemporal features over the global receptive field of the whole pose sequence. All the above design choices cooperating together make our method outperform previous approaches by large margins: 6%-7% on Human3.6M, 5%-10% on CMU-MoCap, and 13%-16% on 3DPW.","url_abs":"https://arxiv.org/abs/2203.16051v1","url_pdf":"https://arxiv.org/pdf/2203.16051v1.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":"progressively-generating-better-initial","repo_url":"https://github.com/705062791/pgbig","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"human-pose-forecasting","task_name":"Human Pose Forecasting"},{"task_slug":"human-motion-prediction","task_name":"Human motion prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/human-pose-forecasting-on-human36m","task":"Human Pose Forecasting","dataset":"Human3.6M","model":"PGBIG","rank_in_archive_order":10,"of":33,"metrics":{"Average MPJPE (mm) @ 1000 ms":"110.3","Average MPJPE (mm) @ 400ms":"58.5","MAR, walking, 1,000ms":"0.69","MAR, walking, 400ms":"0.54"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.16051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16051"}},"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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