Fast Neural Style Transfer for Motion Data
Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-f...
Uložené v:
| Vydané v: | IEEE computer graphics and applications Ročník 37; číslo 4; s. 42 - 49 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Magazine Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
IEEE
2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 0272-1716, 1558-1756, 1558-1756 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-forward neural network trained on a large motion database. The proposed framework can transform the style of motion thousands of times faster than previous approaches that use optimization. |
|---|---|
| AbstractList | Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-forward neural network trained on a large motion database. The proposed framework can transform the style of motion thousands of times faster than previous approaches that use optimization. Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-forward neural network trained on a large motion database. The proposed framework can transform the style of motion thousands of times faster than previous approaches that use optimization.Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-forward neural network trained on a large motion database. The proposed framework can transform the style of motion thousands of times faster than previous approaches that use optimization. |
| Author | Kusajima, Ikuo Habibie, Ikhsanul Holden, Daniel Komura, Taku |
| Author_xml | – sequence: 1 givenname: Daniel surname: Holden fullname: Holden, Daniel email: s0822954@sms.ed.ac.uk organization: Univ. of Edinburgh, Edinburgh, UK – sequence: 2 givenname: Ikhsanul surname: Habibie fullname: Habibie, Ikhsanul email: abie.ikhsan@gmail.com organization: Univ. of Edinburgh, Edinburgh, UK – sequence: 3 givenname: Ikuo surname: Kusajima fullname: Kusajima, Ikuo email: kusajima@ynl.t.u-tokyo.ac.jp organization: Univ. of Tokyo, Tokyo, Japan – sequence: 4 givenname: Taku surname: Komura fullname: Komura, Taku email: tkomura@ed.ac.uk organization: Univ. of Edinburgh, Edinburgh, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28829292$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kM1LwzAYh4NMdJveBUEKXkTYzJuvJkeZbgqbHpznkrZvoNK1M2kP--9t2dzBg-Tw5vD83o9nRAZVXSEhV0CnANQ8rGaLKaMQTzmLQShxQoYgpZ5ALNWADCmLWfcHdU5GIXxRSqUEekbOmdbMdG9I7uc2NNEbtt6W0UezKzFae1sFhz5ytY9WdVPUVfRkG3tBTp0tA14e6ph8zp_Xs5fJ8n3xOntcTjIBvJnEVGiTchM7kbk0lSozDBQYk-VGGYlKOrQogHFjUNDMOe1ymavcxiitS_mY3O37bn393WJokk0RMixLW2HdhgQMByY4NbpDb_-gX3Xrq267nqJa90d21M2BatMN5snWFxvrd8mvhQ6geyDzdQge3REBmvSik0500otODqK7iPoTyYrG9q4ab4vyv-D1Plgg4nGOpsBFLPkPkzSHEw |
| CODEN | ICGADZ |
| CitedBy_id | crossref_primary_10_1016_j_cogsys_2021_11_003 crossref_primary_10_1145_3386569_3392469 crossref_primary_10_1145_3463499 crossref_primary_10_1016_j_entcom_2023_100625 crossref_primary_10_1016_j_patcog_2022_108894 crossref_primary_10_1111_cgf_15169 crossref_primary_10_1016_j_cogsys_2023_05_010 crossref_primary_10_3233_JIFS_224175 crossref_primary_10_1109_ACCESS_2019_2917609 crossref_primary_10_1016_j_cag_2022_11_008 crossref_primary_10_1111_cgf_13946 crossref_primary_10_1145_3522618 crossref_primary_10_1145_3516429 crossref_primary_10_1016_j_procs_2023_10_124 crossref_primary_10_1109_TAFFC_2019_2906167 crossref_primary_10_1016_j_ins_2020_08_060 crossref_primary_10_1016_j_entcom_2019_100300 crossref_primary_10_1111_cgf_70093 crossref_primary_10_3390_computers10030038 crossref_primary_10_1109_TSMC_2024_3502498 crossref_primary_10_3390_electronics13101970 crossref_primary_10_1080_10826068_2021_1980799 crossref_primary_10_1007_s00138_023_01399_x crossref_primary_10_1111_cgf_13555 crossref_primary_10_1111_cgf_14645 crossref_primary_10_1109_TVCG_2023_3320216 crossref_primary_10_1111_cgf_14426 crossref_primary_10_3390_s23052597 crossref_primary_10_1145_3432199 crossref_primary_10_1007_s11042_024_18238_4 crossref_primary_10_1145_3480145 crossref_primary_10_1007_s13042_021_01304_w crossref_primary_10_1109_TAFFC_2022_3226252 crossref_primary_10_1145_3340254 crossref_primary_10_1002_cav_1996 |
| Cites_doi | 10.1145/566654.566608 10.1145/2820903.2820918 10.1145/2766999 10.1109/CVPR.2015.7298965 10.1109/ICCV.2015.494 10.1145/1730804.1730811 10.1145/1073204.1073315 10.1145/1553374.1553505 10.1145/2897824.2925975 10.1145/2897824.2925955 10.1145/218380.218419 10.1145/218380.218421 10.1109/ICCV.2015.55 |
| ContentType | Magazine Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 8FD JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/MCG.2017.3271464 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef PubMed Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | PubMed Computer and Information Systems Abstracts MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-1756 |
| EndPage | 49 |
| ExternalDocumentID | 28829292 10_1109_MCG_2017_3271464 8013475 |
| Genre | orig-research Journal Article |
| GroupedDBID | --- -DZ -~X 0R~ 29I 4.4 5GY 5VS 6IK 85S 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK ACNCT AENEX AETEA AETIX AFFNX AFOGA AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV AZLTO BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 D0L DU5 EBS EJD F5P HZ~ IBMZZ ICLAB IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 MVM O9- OCL P2P RIA RIE RNI RNS RZB TN5 VH1 XJT YYQ YZZ ZY4 AAYXX CITATION AAYOK ABTAH NPM RIG 7SC 8FD JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c413t-70489b397f4cfbb56c9216199cd9695e65feae412399e40cff8fd5d6da7e5afb3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 71 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000411626600007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0272-1716 1558-1756 |
| IngestDate | Sun Nov 09 12:06:04 EST 2025 Sun Jun 29 12:42:01 EDT 2025 Thu Apr 03 06:58:30 EDT 2025 Tue Nov 18 21:35:38 EST 2025 Sat Nov 29 03:27:45 EST 2025 Wed Aug 27 02:47:53 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c413t-70489b397f4cfbb56c9216199cd9695e65feae412399e40cff8fd5d6da7e5afb3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PMID | 28829292 |
| PQID | 1930889292 |
| PQPubID | 85490 |
| PageCount | 8 |
| ParticipantIDs | crossref_citationtrail_10_1109_MCG_2017_3271464 proquest_miscellaneous_1931243098 proquest_journals_1930889292 pubmed_primary_28829292 ieee_primary_8013475 crossref_primary_10_1109_MCG_2017_3271464 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-00-00 |
| PublicationDateYYYYMMDD | 2017-01-01 |
| PublicationDate_xml | – year: 2017 text: 2017-00-00 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Los Alamitos |
| PublicationTitle | IEEE computer graphics and applications |
| PublicationTitleAbbrev | CG-M |
| PublicationTitleAlternate | IEEE Comput Graph Appl |
| PublicationYear | 2017 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 sutskever (ref17) 0 harvey (ref15) 2015 gatys (ref21) 0 hsu (ref8) 2005; 24 ref2 nair (ref20) 0 xia (ref10) 2015; 34 gatys (ref1) 2015 dong (ref11) 0 ref16 ref19 ref18 ref7 zeiler (ref14) 0 ref9 kingma (ref22) 2014 ref4 ref6 ref5 johnson (ref3) 2016 |
| References_xml | – ident: ref6 doi: 10.1145/566654.566608 – ident: ref19 doi: 10.1145/2820903.2820918 – start-page: 184 year: 0 ident: ref11 article-title: Learning a Deep Convolutional Network for Image Super-Resolution publication-title: Proc European Conf Computer Vision – year: 2015 ident: ref1 article-title: A Neural Algorithm of Artistic Style publication-title: ArXiv Preprint – start-page: 807 year: 0 ident: ref20 article-title: Rectified Linear Units Improve Restricted Boltzmann Machines publication-title: Proc 27th Int'l Conf Machine Learning (ICML) – volume: 34 year: 2015 ident: ref10 article-title: Realtime Style Transfer for Unlabeled Heterogeneous Human Motion publication-title: ACM Trans Graphics doi: 10.1145/2766999 – ident: ref13 doi: 10.1109/CVPR.2015.7298965 – ident: ref18 doi: 10.1109/ICCV.2015.494 – ident: ref9 doi: 10.1145/1730804.1730811 – start-page: 262 year: 0 ident: ref21 article-title: Texture Synthesis Using Convolutional Neural Networks publication-title: Proc Advances in Neural Information Processing Systems (NIPS) – year: 2016 ident: ref3 article-title: Perceptual Losses for Real-Time Style Transfer and Super-Resolution publication-title: ArXiv Preprint – volume: 24 start-page: 1082 year: 2005 ident: ref8 article-title: Style Translation for Human Motion publication-title: ACM Trans Graphics doi: 10.1145/1073204.1073315 – ident: ref16 doi: 10.1145/1553374.1553505 – year: 2015 ident: ref15 article-title: Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences publication-title: ArXiv Preprint – start-page: 818 year: 0 ident: ref14 article-title: Visualizing and Understanding Convolutional Networks publication-title: Proc European Conf Computer Vision – ident: ref2 doi: 10.1145/2897824.2925975 – year: 2014 ident: ref22 article-title: Adam: A Method for Stochastic Optimization publication-title: ArXiv Preprint – ident: ref7 doi: 10.1145/2897824.2925955 – start-page: 1601 year: 0 ident: ref17 article-title: The Recurrent Temporal Restricted Boltzmann Machine publication-title: Proc Advances in Neural Information Processing Systems (NIPS) – ident: ref4 doi: 10.1145/218380.218419 – ident: ref5 doi: 10.1145/218380.218421 – ident: ref12 doi: 10.1109/ICCV.2015.55 |
| SSID | ssj0005510 |
| Score | 1.4096133 |
| Snippet | Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 42 |
| SubjectTerms | Analytical models Animation computer graphics Data models Data transfer (computers) deep learning Human motion machine learning motion capture Motion control Neural networks Optimization style transfer Training Virtual reality |
| Title | Fast Neural Style Transfer for Motion Data |
| URI | https://ieeexplore.ieee.org/document/8013475 https://www.ncbi.nlm.nih.gov/pubmed/28829292 https://www.proquest.com/docview/1930889292 https://www.proquest.com/docview/1931243098 |
| Volume | 37 |
| WOSCitedRecordID | wos000411626600007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fa9swED7a0IftJV3Trt6y4sJeVuZGkWXLeizdsr60DNpB3owsnWAQkpI4hf73Pck_2sE62IsxWLbF3Un36XS6D-BzYfNUaGESlmtaoAiGCfl1ljjMuZUpmbUWgWxC3twU87n6uQNf-7MwiBiSz_Dc34a9fLsyWx8qm9BsmgqZ7cKulLI5q_WczpFNm3iK5IkvAdNtSTI1ub784XO45HnKJU0MnoqHE7AkYMD_8EaBXuV1pBk8zmz4f33dh2FXKTq-aGzhHezg8gDevig4OIKzmd7UsS_IoRfxbf24wDg4K4frmNBrfB04feJvutaH8Gv2_e7yKmnZEhJDjqhOJI1FVRG8cMK4qspyozjBOaWMVbnKMM8cahRTf5gVBTPOFc5mNrdaYqZdlR7BYLla4jHEhUN0tihSugrDKo3cMOGknRqRWZ5HMOmkVpq2lLhntFiUYUnBVEkiL73Iy1bkEXzp37hvymj8o-3Ii7Nv10oygnGnmLIdZ5uS4KfP0yJNRnDaP6YR4rc99BJX29CGQEzKVBHB-0ah_bc7O_jw939-hDe-Z03IZQyDer3FT7BnHurfm_UJmeG8OAlm-AT_0NR8 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3faxQxEB5qK6gvVat2teoKvihuL5dNNpvHUnu2tHcIVujbkk0mIBx35W5P8L93kv1hhVrwZVnY2d0wmcl8SSbzAbwvXZELI2zGCkMTFMEwo7jOMo8FdyonszYikk2o2ay8utJft-DTcBYGEWPyGR6G27iX75Z2E5bKRjSa5kLJe7AjheDj9rTWn4QOOW5XVBTPQhGYflOS6dH0-EvI4lKHOVc0NAQyHk7QkqAB_yseRYKVf2PNGHMmu__X2sew29eKTo9aa3gCW7h4Co9ulBzcg48Ts27SUJLDzNNvza85pjFceVylhF_TaWT1ST-bxjyD75OTy-PTrONLyCyFoiZT5I26JoDhhfV1LQurOQE6ra3ThZZYSI8GxTgcZ0XBrPeld9IVziiUxtf5c9heLBe4D2npEb0ry5yuwrLaILdMeOXGVkjHiwRGvdYq2xUTD5wW8ypOKpiuSOVVUHnVqTyBD8Mb120hjTtk94I6B7lOkwkc9B1TdZ62rgiAhkwt6skE3g2PyUfCxodZ4HITZQjG5EyXCbxoO3T4dm8HL2__51t4cHo5vaguzmbnr-BhaGW7AHMA281qg6_hvv3Z_Fiv3kRj_A01-9bb |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fast+Neural+Style+Transfer+for+Motion+Data&rft.jtitle=IEEE+computer+graphics+and+applications&rft.au=Holden%2C+Daniel&rft.au=Habibie%2C+Ikhsanul&rft.au=Kusajima%2C+Ikuo&rft.au=Komura%2C+Taku&rft.date=2017-01-01&rft.eissn=1558-1756&rft.volume=37&rft.issue=4&rft.spage=42&rft_id=info:doi/10.1109%2FMCG.2017.3271464&rft_id=info%3Apmid%2F28829292&rft.externalDocID=28829292 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0272-1716&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0272-1716&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0272-1716&client=summon |