{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:22Z","timestamp":1761176122505,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Human motion prediction from observed 3D skeleton sequences is a challenging task in computer vision, primarily due to the difficulty of learning robust and universal feature representations. This paper introduces a novel self-supervised learning framework for human motion prediction, pioneering the use of masked reconstruction pretraining in this domain. Our framework comprises two phases: pretraining and formal training. Both phases segment motion sequences into temporal patches to enable efficient encoding, where pretraining randomly masks patch subsets to reconstruct the full sequence using unmasked patches, and formal training is formulated as an extreme special case of pretraining, where the entire future motion sequence to be predicted is masked as a reconstruction task. To address the loss of fine-grained features caused by the patch-based encoding strategy, we propose a Transformer model with masked skip-connections designed to complement our learning framework. Extensive evaluations on the Human3.6M and 3DPW datasets show that our approach surpasses state-of-the-art methods, achieving reductions in Mean Per Joint Position Error (MPJPE) of 4.3% and 4.1%, respectively.<\/jats:p>","DOI":"10.3233\/faia250824","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:13Z","timestamp":1761126193000},"source":"Crossref","is-referenced-by-count":0,"title":["Masked Patch Skip-Connection Transformer for Human Motion Prediction"],"prefix":"10.3233","author":[{"given":"Youhuang","family":"Guo","sequence":"first","affiliation":[{"name":"Soochow University"}]},{"given":"Hong","family":"Gao","sequence":"additional","affiliation":[{"name":"Soochow University"}]},{"given":"Yuqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}]},{"given":"Zhijun","family":"Li","sequence":"additional","affiliation":[{"name":"Soochow University"},{"name":"Harbin Institute of Technology"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250824","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:13Z","timestamp":1761126193000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250824"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250824","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}