{"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/multi-person-3d-motion-prediction-with-multi-1","title":"Multi-Person 3D Motion Prediction with Multi-Range Transformers","arxiv_id":"2111.12073","date":"2021-11-23","proceeding":"NeurIPS 2021 12","authors":["Jiashun Wang","Huazhe Xu","Medhini Narasimhan","Xiaolong Wang"],"abstract":"We propose a novel framework for multi-person 3D motion trajectory prediction. Our key observation is that a human's action and behaviors may highly depend on the other persons around. Thus, instead of predicting each human pose trajectory in isolation, we introduce a Multi-Range Transformers model which contains of a local-range encoder for individual motion and a global-range encoder for social interactions. The Transformer decoder then performs prediction for each person by taking a corresponding pose as a query which attends to both local and global-range encoder features. Our model not only outperforms state-of-the-art methods on long-term 3D motion prediction, but also generates diverse social interactions. More interestingly, our model can even predict 15-person motion simultaneously by automatically dividing the persons into different interaction groups. Project page with code is available at https://jiashunwang.github.io/MRT/.","url_abs":"https://arxiv.org/abs/2111.12073v1","url_pdf":"https://arxiv.org/pdf/2111.12073v1.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":"multi-person-3d-motion-prediction-with-multi-1","repo_url":"https://github.com/jiashunwang/MRT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"multi-person-pose-forecasting","task_name":"Multi-Person Pose forecasting"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-person-pose-forecasting-on-expi-common","task":"Multi-Person Pose forecasting","dataset":"Expi - common actions split","model":"MRT","rank_in_archive_order":4,"of":6,"metrics":{"Average MPJPE (mm) @ 1000 ms":"238","Average MPJPE (mm) @ 200 ms":"58","Average MPJPE (mm) @ 400 ms":"116","Average MPJPE (mm) @ 600 ms":"163"},"uses_additional_data":false},{"leaderboard":"/sota/multi-person-pose-forecasting-on-expi-unseen","task":"Multi-Person Pose forecasting","dataset":"Expi - unseen actions split","model":"MRT","rank_in_archive_order":4,"of":5,"metrics":{"Average MPJPE (mm) @ 400 ms":"146","Average MPJPE (mm) @ 600 ms":"205","Average MPJPE (mm) @ 800 ms":"291"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.12073","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}