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        <identifier>oai:ipsj.ixsq.nii.ac.jp:00175433</identifier>
        <datestamp>2025-01-20T06:14:06Z</datestamp>
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          <dc:title>Enhancing Subspaces for Elastic Deformation with Collisions</dc:title>
          <dc:title xml:lang="en">Enhancing Subspaces for Elastic Deformation with Collisions</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>Duosheng, Yu</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>Takashi, Kanai</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Duosheng, Yu</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Takashi, Kanai</jpcoar:creatorName>
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          <datacite:description descriptionType="Other">In general, subspace methods for elastic deformations can greatly increase the simulation speed and have good global supports. However, when novel external collisions are encountered, the expressivity of subspace is not enough, and then obvious artifacts will appear. In this paper, we present an efficient data-driven approach to adaptively enhance the expressivity of subspace for elastic deformations with novel collisions. We firstly capture several time-series shapes from an object by performing full-space simulation. We then construct a database of subspace including displacements and potential derivatives among shapes of all time-series. In run-time simulation, we choose several proper bases from such a database and use them as run-time local basis. As a result, we show that our approach can achieve faster and more accurate computation of elastic deformation when novel collisions happen.</datacite:description>
          <datacite:description descriptionType="Other">In general, subspace methods for elastic deformations can greatly increase the simulation speed and have good global supports. However, when novel external collisions are encountered, the expressivity of subspace is not enough, and then obvious artifacts will appear. In this paper, we present an efficient data-driven approach to adaptively enhance the expressivity of subspace for elastic deformations with novel collisions. We firstly capture several time-series shapes from an object by performing full-space simulation. We then construct a database of subspace including displacements and potential derivatives among shapes of all time-series. In run-time simulation, we choose several proper bases from such a database and use them as run-time local basis. As a result, we show that our approach can achieve faster and more accurate computation of elastic deformation when novel collisions happen.</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2016-11-02</datacite:date>
          <dc:language>eng</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_18gh">technical report</dc:type>
          <jpcoar:identifier identifierType="URI">https://ipsj.ixsq.nii.ac.jp/records/175433</jpcoar:identifier>
          <jpcoar:sourceIdentifier identifierType="ISSN">2188-8701</jpcoar:sourceIdentifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AA11131797</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>研究報告コンピュータビジョンとイメージメディア（CVIM）</jpcoar:sourceTitle>
          <jpcoar:volume>2016-CVIM-204</jpcoar:volume>
          <jpcoar:issue>24</jpcoar:issue>
          <jpcoar:pageStart>1</jpcoar:pageStart>
          <jpcoar:pageEnd>6</jpcoar:pageEnd>
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            <datacite:date dateType="Available">2018-11-02</datacite:date>
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