{"id":211114,"updated":"2025-01-19T17:54:36.368204+00:00","links":{},"created":"2025-01-19T01:12:18.249085+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211114","sets":["1164:4179:10535:10590"]},"path":["10590"],"owner":"44499","recid":"211114","title":["部位の領域分割画像を入力とした微分可能レンダラによる人体の三次元再構成"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-05-13"},"_buckets":{"deposit":"75dca5b4-61b7-4d60-83c8-f575c3b01ec9"},"_deposit":{"id":"211114","pid":{"type":"depid","value":"211114","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"部位の領域分割画像を入力とした微分可能レンダラによる人体の三次元再構成","author_link":["535857","535854","535852","535855","535856","535853"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"部位の領域分割画像を入力とした微分可能レンダラによる人体の三次元再構成"},{"subitem_title":"3D human pose and shape reconstruction with differentiable renderer from body part segmentation","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"画像","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-05-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学情報理工学院"},{"subitem_text_value":"東京工業大学情報理工学院"},{"subitem_text_value":"東京工業大学情報理工学院"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"School of Computing, Tokyo Institute of Technology ","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/211114/files/IPSJ-NL21248014.pdf","label":"IPSJ-NL21248014.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL21248014.pdf","filesize":[{"value":"2.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"23"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"f438e533-70ff-4a92-8e89-f6d15c14efb5","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"櫻井, 凜太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宇都, 有昭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"篠田, 浩一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Rintaro, Sakurai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kuniaki, Uto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Koichi, Shinoda","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10115061","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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8779","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"三次元人体再構成のための深層学習の訓練データとして，二次元信号から復元された三次元人体形状がしばしば用いられる．本研究では部位の領域分割画像が与えられる場合に，部位ごとの相互隠蔽を考慮したシルエット を微分可能レンダラによって作成し，勾配法によって人体形状を精確に推定する手法を提案する．部位の領域分割画像には，全身のシルエットが持たない姿勢の情報と，二次元関節座標がもたない体型の情報が含まれる．3D Poses in the Wild dataset (3DPW) を用いた実験では部位の領域分割画像と二次元関節座標を併用した場合に，三次元関節座標誤差と三次元頂点座標誤差について，従来手法 SMPLify による最適解の誤差が 0.224 と 0.191 であったのに対して，提案手法では 0.210 と 0.184 であった．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-05-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"14","bibliographicVolumeNumber":"2021-NL-248"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}