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Graph."],"published-print":{"date-parts":[[2025,8,1]]},"abstract":"<jats:p>\n                    In this paper, we propose a novel dynamic calibration method for sparse inertial motion capture systems, which is the first to break the restrictive\n                    <jats:italic toggle=\"yes\">absolute static assumption<\/jats:italic>\n                    in IMU calibration, i.e., the coordinate drift\n                    <jats:italic toggle=\"yes\">\n                      R\n                      <jats:sub>G\u2032 G<\/jats:sub>\n                    <\/jats:italic>\n                    and measurement offset\n                    <jats:italic toggle=\"yes\">\n                      R\n                      <jats:sub>BS<\/jats:sub>\n                    <\/jats:italic>\n                    remain constant during the entire motion, thereby significantly expanding their application scenarios. Specifically, we achieve real-time estimation of\n                    <jats:italic toggle=\"yes\">\n                      R\n                      <jats:sub>G\u2032 G<\/jats:sub>\n                    <\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">\n                      R\n                      <jats:sub>BS<\/jats:sub>\n                    <\/jats:italic>\n                    under two relaxed assumptions: i) the matrices change negligibly in a short time window; ii) the human movements\/IMU readings are diverse in such a time window. Intuitively, the first assumption reduces the number of candidate matrices, and the second assumption provides diverse constraints, which greatly reduces the solution space and allows for accurate estimation of\n                    <jats:italic toggle=\"yes\">\n                      R\n                      <jats:sub>G\u2032 G<\/jats:sub>\n                    <\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">\n                      R\n                      <jats:sub>BS<\/jats:sub>\n                    <\/jats:italic>\n                    from a short history of IMU readings in real time. To achieve this, we created synthetic datasets of paired\n                    <jats:italic toggle=\"yes\">\n                      R\n                      <jats:sub>G\u2032 G<\/jats:sub>\n                      , R\n                      <jats:sub>BS<\/jats:sub>\n                    <\/jats:italic>\n                    matrices and IMU readings, and learned their mappings using a Transformer-based model. We also designed a calibration trigger based on the diversity of IMU readings to ensure that assumption ii) is met before applying our method. To our knowledge, we are the first to achieve implicit IMU calibration (i.e., seamlessly putting IMUs into use without the need for an explicit calibration process), as well as the first to enable long-term and accurate motion capture using sparse IMUs. The code and dataset are available at\n                    <jats:italic toggle=\"yes\">https:\/\/github.com\/ZuoCX1996\/TIC.<\/jats:italic>\n                  <\/jats:p>","DOI":"10.1145\/3730937","type":"journal-article","created":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T04:02:22Z","timestamp":1753588942000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2054-2010","authenticated-orcid":false,"given":"Chengxu","family":"Zuo","sequence":"first","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9804-1606","authenticated-orcid":false,"given":"Jiawei","family":"Huang","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3776-8164","authenticated-orcid":false,"given":"Xiao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4363-1294","authenticated-orcid":false,"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4797-7188","authenticated-orcid":false,"given":"Xiangren","family":"Shi","sequence":"additional","affiliation":[{"name":"Bournemouth University, Bournemouth, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6182-1381","authenticated-orcid":false,"given":"Rui","family":"Cao","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3504-3222","authenticated-orcid":false,"given":"Xinyu","family":"Yi","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0953-1057","authenticated-orcid":false,"given":"Feng","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1473-297X","authenticated-orcid":false,"given":"Shihui","family":"Guo","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1551-9126","authenticated-orcid":false,"given":"Yipeng","family":"Qin","sequence":"additional","affiliation":[{"name":"Cardiff University, Cardiff, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2025,7,27]]},"reference":[{"key":"e_1_2_2_1_1","first-page":"2","volume-title":"Proc. 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