{"updated":"2025-01-22T02:24:39.325248+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00062829","sets":["1164:4619:5663:5716"]},"path":["5716"],"owner":"10","recid":"62829","title":["Keypose and Style Analysis Based on Low-dimensional Representation"],"pubdate":{"attribute_name":"公開日","attribute_value":"2009-06-02"},"_buckets":{"deposit":"92a9b696-1505-4f14-a619-2615deed3ae8"},"_deposit":{"id":"62829","pid":{"type":"depid","value":"62829","revision_id":0},"owners":[10],"status":"published","created_by":10},"item_title":"Keypose and Style Analysis Based on Low-dimensional Representation","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Keypose and Style Analysis Based on Low-dimensional Representation"},{"subitem_title":"Keypose and Style Analysis Based on Low-dimensional Representation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"D論セッション1","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2009-06-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/62829/files/IPSJ-CVIM09167002.pdf"},"date":[{"dateType":"Available","dateValue":"2011-06-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM09167002.pdf","filesize":[{"value":"1.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"6bf756dd-5d25-4121-997a-7bc8063639fd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2009 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Manoj, Perera"},{"creatorName":"Shunsuke, Kudoh"},{"creatorName":"Katsushi, Ikeuchi"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Manoj, Perera","creatorNameLang":"en"},{"creatorName":"Shunsuke, Kudoh","creatorNameLang":"en"},{"creatorName":"Katsushi, Ikeuchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Human motion analysis is a complex but extremely interesting and important research area in computer graphics and computer vision. This paper addresses three vital topics in human motion analysis related to keyposes: how to extract keyposes from dance motions, how to utilize them, and how to recognize the person and task that constitute a keypose. As our first topic, we propose a new method to extract keyposes from a given dance using the energy flow of the motion. Our experimental results and comparison with a previous keypose extraction approach show the high accuracy of keypose extraction with our new method. As our second topic, we propose a new method to reconstruct low-dimensional motion based on keyposes, and we illustrate the effect of keyposes in a given motion space on human perception. We utilize the keyposes extracted with our new method, formulate a model, and derive a low-dimensional motion based on our model. We also construct low-dimensional motion using uniform sampling poses, and we compare the results with those obtained from our method. As our third topic, we propose a novel approach to decompose motion into common and individual factors using the Multi Factor Tensor (MFT) model. By this method, we recognize person and task from the motion sequence.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Human motion analysis is a complex but extremely interesting and important research area in computer graphics and computer vision. This paper addresses three vital topics in human motion analysis related to keyposes: how to extract keyposes from dance motions, how to utilize them, and how to recognize the person and task that constitute a keypose. As our first topic, we propose a new method to extract keyposes from a given dance using the energy flow of the motion. Our experimental results and comparison with a previous keypose extraction approach show the high accuracy of keypose extraction with our new method. As our second topic, we propose a new method to reconstruct low-dimensional motion based on keyposes, and we illustrate the effect of keyposes in a given motion space on human perception. We utilize the keyposes extracted with our new method, formulate a model, and derive a low-dimensional motion based on our model. We also construct low-dimensional motion using uniform sampling poses, and we compare the results with those obtained from our method. As our third topic, we propose a novel approach to decompose motion into common and individual factors using the Multi Factor Tensor (MFT) model. By this method, we recognize person and task from the motion sequence.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"16","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2009-06-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2009-CVIM-167"}]},"relation_version_is_last":true,"weko_creator_id":"10"},"created":"2025-01-18T23:24:42.526430+00:00","id":62829,"links":{}}