{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:59:28Z","timestamp":1775325568773,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/111","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T22:55:56Z","timestamp":1657925756000},"page":"791-798","source":"Crossref","is-referenced-by-count":59,"title":["MotionMixer: MLP-based 3D Human Body Pose Forecasting"],"prefix":"10.24963","author":[{"given":"Arij","family":"Bouazizi","sequence":"first","affiliation":[{"name":"Mercedes-Benz AG, Stuttgart, Germany"},{"name":"Ulm University, Ulm, Germany"}]},{"given":"Adrian","family":"Holzbock","sequence":"additional","affiliation":[{"name":"Ulm University, Ulm, Germany"}]},{"given":"Ulrich","family":"Kressel","sequence":"additional","affiliation":[{"name":"Mercedes-Benz AG, Stuttgart, Germany"}]},{"given":"Klaus","family":"Dietmayer","sequence":"additional","affiliation":[{"name":"Ulm University, Ulm, Germany"}]},{"given":"Vasileios","family":"Belagiannis","sequence":"additional","affiliation":[{"name":"Otto von Guericke University Magdeburg, Magdeburg, Germany"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T07:07:45Z","timestamp":1658128065000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/111"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/111","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}