{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T20:03:23Z","timestamp":1772309003094,"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":[[2020,7]]},"abstract":"<jats:p>This paper proposes a novel Semi-Dynamic Hypergraph Neural Network (SD-HNN) to estimate 3D human pose from a single image. SD-HNN adopts hypergraph to represent the human body to effectively exploit the kinematic constrains among adjacent and non-adjacent joints. Specifically, a pose hypergraph in SD-HNN has two components. One is a static hypergraph constructed according to the conventional tree body structure. The other is the semi-dynamic hypergraph representing the dynamic kinematic constrains among different joints. These two hypergraphs are combined together to be trained in an end-to-end fashion. Unlike traditional Graph Convolutional Networks (GCNs) that are based on a fixed tree structure, the SD-HNN can deal with ambiguity in human pose estimation. Experimental results demonstrate that the proposed method achieves state-of-the-art performance both on the Human3.6M and MPI-INF-3DHP datasets.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/109","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"782-788","source":"Crossref","is-referenced-by-count":31,"title":["Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation"],"prefix":"10.24963","author":[{"given":"Shengyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Zhengzhou University"}]},{"given":"Pei","family":"Lv","sequence":"additional","affiliation":[{"name":"Zhengzhou University"}]},{"given":"Yuzhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhengzhou University"}]},{"given":"Jie","family":"Fu","sequence":"additional","affiliation":[{"name":"Zhengzhou University"}]},{"given":"Junjin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Zhengzhou University"}]},{"given":"Wanqing","family":"Li","sequence":"additional","affiliation":[{"name":"University of Wollongong"}]},{"given":"Bing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhengzhou University"}]},{"given":"Mingliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhengzhou University"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:13:20Z","timestamp":1594260800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/109"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/109","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}