{"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/motionmixer-mlp-based-3d-human-body-pose","title":"MotionMixer: MLP-based 3D Human Body Pose Forecasting","arxiv_id":"2207.00499","date":"2022-07-01","proceeding":null,"authors":["Arij Bouazizi","Adrian Holzbock","Ulrich Kressel","Klaus Dietmayer","Vasileios Belagiannis"],"abstract":"In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine grained spatial dependencies of the body joints. The interaction of the body joints over time is then modelled by a temporal MLP. The spatial-temporal mixed features are finally aggregated and decoded to obtain the future motion. To calibrate the influence of each time step in the pose sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation protocols. For all evaluations, we demonstrate state-of-the-art performance, while having a model with a smaller number of parameters. Our code is available at: https://github.com/MotionMLP/MotionMixer","url_abs":"https://arxiv.org/abs/2207.00499v1","url_pdf":"https://arxiv.org/pdf/2207.00499v1.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":"motionmixer-mlp-based-3d-human-body-pose","repo_url":"https://github.com/motionmlp/motionmixer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"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":"MotionMixer","rank_in_archive_order":11,"of":33,"metrics":{"Average MPJPE (mm) @ 1000 ms":"111.0","Average MPJPE (mm) @ 400ms":"59.3","MAR, walking, 1,000ms":"0.73","MAR, walking, 400ms":"0.58"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2207.00499","atlas_url":"https://app.syntology.ai/?focus=2207.00499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.00499"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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