NNWarp: Neural Network-Based Nonlinear Deformation
NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g., an array of all the pixels in an image), the input...
Uloženo v:
| Vydáno v: | IEEE transactions on visualization and computer graphics Ročník 26; číslo 4; s. 1745 - 1759 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1077-2626, 1941-0506, 1941-0506 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g., an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though the neural network is known for its rich expressivity of nonlinear functions, directly using an NN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. NNWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model-the linear elasticity. In other words, NNWarp yields an incremental displacement fix per mesh node based on a simplified (therefore incorrect) simulation result other than synthesizing the unknown displacement directly. We introduce a compact yet effective feature vector including geodesic , potential and digression to sort training pairs of per-node linear and nonlinear displacement. NNWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one NN training is able to handle a wide range of 3D models of various geometries. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, which allows NNWarp to simulate large models in real time. |
|---|---|
| AbstractList | NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g., an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though the neural network is known for its rich expressivity of nonlinear functions, directly using an NN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. NNWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model–the linear elasticity. In other words, NNWarp yields an incremental displacement fix per mesh node based on a simplified (therefore incorrect) simulation result other than synthesizing the unknown displacement directly. We introduce a compact yet effective feature vector including geodesic , potential and digression to sort training pairs of per-node linear and nonlinear displacement. NNWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one NN training is able to handle a wide range of 3D models of various geometries. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, which allows NNWarp to simulate large models in real time. NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g., an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though the neural network is known for its rich expressivity of nonlinear functions, directly using an NN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. NNWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model-the linear elasticity. In other words, NNWarp yields an incremental displacement fix per mesh node based on a simplified (therefore incorrect) simulation result other than synthesizing the unknown displacement directly. We introduce a compact yet effective feature vector including geodesic, potential and digression to sort training pairs of per-node linear and nonlinear displacement. NNWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one NN training is able to handle a wide range of 3D models of various geometries. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, which allows NNWarp to simulate large models in real time.NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g., an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though the neural network is known for its rich expressivity of nonlinear functions, directly using an NN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. NNWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model-the linear elasticity. In other words, NNWarp yields an incremental displacement fix per mesh node based on a simplified (therefore incorrect) simulation result other than synthesizing the unknown displacement directly. We introduce a compact yet effective feature vector including geodesic, potential and digression to sort training pairs of per-node linear and nonlinear displacement. NNWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one NN training is able to handle a wide range of 3D models of various geometries. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, which allows NNWarp to simulate large models in real time. |
| Author | Yang, Yin Wang, Huamin Zhou, Kun Xu, Weiwei Shao, Tianjia Luo, Ran Chen, Xiang |
| Author_xml | – sequence: 1 givenname: Ran orcidid: 0000-0003-2232-2775 surname: Luo fullname: Luo, Ran email: luoran@unm.edu organization: Electrical and Computer Engineering Department, University of New Mexico, NM, USA – sequence: 2 givenname: Tianjia orcidid: 0000-0001-5485-3752 surname: Shao fullname: Shao, Tianjia email: tianjiashao@gmail.com organization: School of Computing, University of Leeds, Leeds, United Kingdom – sequence: 3 givenname: Huamin surname: Wang fullname: Wang, Huamin email: whmin@cse.ohio-state.edu organization: Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA – sequence: 4 givenname: Weiwei surname: Xu fullname: Xu, Weiwei email: weiwei.xu.g@gmail.com organization: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China – sequence: 5 givenname: Xiang orcidid: 0000-0002-6955-8729 surname: Chen fullname: Chen, Xiang email: xchen.cs@gmail.com organization: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China – sequence: 6 givenname: Kun orcidid: 0000-0003-4243-6112 surname: Zhou fullname: Zhou, Kun email: kunzhou@acm.org organization: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China – sequence: 7 givenname: Yin orcidid: 0000-0001-7645-5931 surname: Yang fullname: Yang, Yin email: yangy@unm.edu organization: Electrical and Computer Engineering Department, University of New Mexico, NM, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30442607$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kD1PwzAQhi1URMvHD0BIqBILS4rPdmyHDQoUJFSWCkbLcS5SII2LnQjx70lpYWBguhue99Xds08GjW-QkGOgEwCaXSyep7MJo6AnTGsQKeyQEWQCEppSOeh3qlTCJJNDsh_jK6UghM72yJBTIZikakTYfP5iw-pyPMcu2Lof7YcPb8m1jViM576pqwZtGN9g6cPStpVvDsluaeuIR9t5QBZ3t4vpffL4NHuYXj0mjousTcpCWEbLQikrnAAtRcZFbrnLXao4y6mTmEOa6VKmFEoHeVqgpRzyAkWG_ICcb2pXwb93GFuzrKLDurYN-i4aBjwFDpmGHj37g776LjT9cYZxxfpvtZI9dbqlunyJhVmFamnDp_mR0QNqA7jgYwxYGle13y-3wVa1AWrW2s1au1lrN1vtfRL-JH_K_8ucbDIVIv7yOuVSgOJfQxeK1w |
| CODEN | ITVGEA |
| CitedBy_id | crossref_primary_10_1145_3658154 crossref_primary_10_1016_j_media_2019_101569 crossref_primary_10_1109_TG_2023_3237943 crossref_primary_10_1007_s11390_021_1414_9 crossref_primary_10_1145_3386569_3392398 crossref_primary_10_1111_cgf_14128 |
| Cites_doi | 10.1145/37402.37427 10.1145/1073204.1073300 10.1109/TVCG.2012.173 10.1145/1618452.1618469 10.1109/ICCV.2015.357 10.1109/ISCSLP.2012.6423452 10.1145/2766910 10.1145/2601097.2601116 10.1109/TVCG.2005.13 10.1111/j.1467-8659.2012.03230.x 10.1109/CVPR.2014.223 10.1137/1011036 10.1016/j.neunet.2014.09.003 10.1145/2185520.2185566 10.1109/ICCV.2015.178 10.1145/1073204.1073296 10.1145/2461912.2462020 10.1145/3072959.3073685 10.1145/1882261.1866182 10.1145/3072959.3073608 10.1145/1618452.1618516 10.1109/TPAMI.2012.231 10.1111/j.1467-8659.2012.03031.x 10.1109/ICCV.2015.123 10.1145/3072959.3073643 10.1145/1778765.1778844 10.1145/2816795.2818129 10.1109/TVCG.2010.109 10.1145/2461912.2461931 10.1145/2343483.2343495 10.1145/2816795.2818063 10.1007/BF02551274 10.1145/3072959.3073631 10.1145/2766938 10.1145/2897824.2925979 10.1145/1731047.1731054 10.1145/74333.74355 10.1145/2980179.2980236 10.1145/2816795.2818090 10.1007/s11263-015-0816-y 10.1145/2508363.2508406 10.1145/3072959.3073655 10.1038/nature14236 10.1145/545261.545269 10.1145/2980179.2982437 10.1145/2508363.2508392 10.1145/2601097.2601136 10.1145/2816795.2818089 10.1145/2601097.2601217 10.1145/3083723 10.1145/1409060.1409118 10.1145/3072959.3073663 10.1145/2010324.1964988 10.1145/2343483.2343501 10.1016/0893-6080(91)90009-T 10.1145/2990496 10.1145/2766904 10.1145/2766911 10.1145/1882261.1866160 10.1145/1778765.1778776 10.1145/3072959.3073602 10.1145/1964921.1964986 10.1145/2893476 10.1109/TASL.2011.2134090 10.1145/1073204.1073216 10.1145/2010324.1964966 10.1145/1028523.1028541 10.1145/2231816.2231821 10.1145/2461912.2461961 10.1109/38.20317 10.1109/ICASSP.2013.6639081 10.1109/TPAMI.2015.2437384 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TVCG.2018.2881451 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications 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 Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | Technology Research Database MEDLINE - Academic PubMed |
| 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 | 1941-0506 |
| EndPage | 1759 |
| ExternalDocumentID | 30442607 10_1109_TVCG_2018_2881451 8536417 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: Microsoft Research Asia funderid: 10.13039/100006112 – fundername: NSFC grantid: 61732016 – fundername: NSFC grantid: 61772024; 61732016 – fundername: National Science Foundation grantid: CHS-1524992 funderid: 10.13039/100000001 – fundername: NSFC grantid: 61772462; U1736217 – fundername: Fundamental Research Funds for the Central Universities grantid: 2017YFB1002600 funderid: 10.13039/501100012226 – fundername: National Science Foundation grantid: CHS-1717972 funderid: 10.13039/100000001 – fundername: AFRL grantid: FA9453-18-2-0022 – fundername: Alibaba IDEA Lab |
| GroupedDBID | --- -~X .DC 0R~ 29I 4.4 53G 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS TN5 5VS AAYXX AETIX AGSQL AI. AIBXA ALLEH CITATION H~9 IFJZH RNI RZB VH1 AAYOK NPM RIG 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c349t-fd4a20fd77a4c41864934ba3cbc5732b0c6eb1598f6501fc1b5dea031bde49e3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 31 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000519547200010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1077-2626 1941-0506 |
| IngestDate | Thu Sep 25 09:04:11 EDT 2025 Sun Jun 29 16:47:12 EDT 2025 Thu Apr 03 06:56:32 EDT 2025 Sat Nov 29 06:05:40 EST 2025 Tue Nov 18 21:42:11 EST 2025 Wed Aug 27 06:28:53 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c349t-fd4a20fd77a4c41864934ba3cbc5732b0c6eb1598f6501fc1b5dea031bde49e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-4243-6112 0000-0001-5485-3752 0000-0002-6955-8729 0000-0003-2232-2775 0000-0001-7645-5931 |
| PMID | 30442607 |
| PQID | 2372304876 |
| PQPubID | 75741 |
| PageCount | 15 |
| ParticipantIDs | ieee_primary_8536417 proquest_journals_2372304876 crossref_primary_10_1109_TVCG_2018_2881451 crossref_citationtrail_10_1109_TVCG_2018_2881451 pubmed_primary_30442607 proquest_miscellaneous_2135131981 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-04-01 |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: 2020-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on visualization and computer graphics |
| PublicationTitleAbbrev | TVCG |
| PublicationTitleAlternate | IEEE Trans Vis Comput Graph |
| PublicationYear | 2020 |
| 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 | ref57 ref13 ref56 ref12 peng (ref39) 2017; 36 ref58 ref14 ref53 ref55 ref11 ref54 ref10 dechter (ref15) 1986 ref19 sharif razavian (ref16) 2014 wu (ref80) 2015 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref85 ref41 ref44 ref43 chen (ref22) 2016; abs 1606 915 abadi (ref82) 2016; abs 1603 4467 ref49 ref8 ref9 ref4 ref3 ref6 ref5 ref40 ref84 ref83 ref79 ref35 ref78 ref34 ref37 ref75 ref31 ref74 ref30 ref77 ref33 ref32 ref2 ref38 desbrun (ref52) 1999; 99 ref71 ref70 ref73 ref72 simonyan (ref17) 2014; abs 1409 1556 andrásfai (ref76) 1977 narain (ref59) 2016 ref68 ref24 ref67 ref23 ref26 ref69 ref25 ref64 ref20 ref63 kingma (ref81) 2014; abs 1412 6980 ref66 ref65 ref21 ref28 ref27 ref29 krizhevsky (ref18) 2012 kristan (ref7) 2015 bathe (ref1) 2008 liu (ref36) 2016; 35 ref60 ref62 ref61 |
| References_xml | – ident: ref48 doi: 10.1145/37402.37427 – ident: ref64 doi: 10.1145/1073204.1073300 – ident: ref73 doi: 10.1109/TVCG.2012.173 – ident: ref66 doi: 10.1145/1618452.1618469 – ident: ref6 doi: 10.1109/ICCV.2015.357 – ident: ref5 doi: 10.1109/ISCSLP.2012.6423452 – ident: ref35 doi: 10.1145/2766910 – ident: ref10 doi: 10.1145/2601097.2601116 – ident: ref13 doi: 10.1109/TVCG.2005.13 – ident: ref83 doi: 10.1111/j.1467-8659.2012.03230.x – ident: ref20 doi: 10.1109/CVPR.2014.223 – ident: ref72 doi: 10.1137/1011036 – ident: ref2 doi: 10.1016/j.neunet.2014.09.003 – volume: abs 1603 4467 year: 2016 ident: ref82 article-title: Tensorflow: Large-scale machine learning on heterogeneous distributed systems publication-title: CoRR – ident: ref70 doi: 10.1145/2185520.2185566 – ident: ref23 doi: 10.1109/ICCV.2015.178 – ident: ref50 doi: 10.1145/1073204.1073296 – ident: ref31 doi: 10.1145/2461912.2462020 – year: 1977 ident: ref76 publication-title: Introductory Graph Theory – ident: ref43 doi: 10.1145/3072959.3073685 – start-page: 21 year: 2016 ident: ref59 article-title: Admm $\supseteq$? projective dynamics: Fast simulation of general constitutive models publication-title: Proc ACM SIGGRAPH/Eurographics Symp Comput Animation – ident: ref55 doi: 10.1145/1882261.1866182 – ident: ref79 doi: 10.1145/3072959.3073608 – ident: ref33 doi: 10.1145/1618452.1618516 – volume: abs 1409 1556 year: 2014 ident: ref17 article-title: Very deep convolutional networks for large-scale image recognition publication-title: CoRR – ident: ref19 doi: 10.1109/TPAMI.2012.231 – ident: ref30 doi: 10.1111/j.1467-8659.2012.03031.x – ident: ref21 doi: 10.1109/ICCV.2015.123 – ident: ref47 doi: 10.1145/3072959.3073643 – ident: ref27 doi: 10.1145/1778765.1778844 – ident: ref46 doi: 10.1145/2816795.2818129 – ident: ref67 doi: 10.1109/TVCG.2010.109 – start-page: 1912 year: 2015 ident: ref80 article-title: 3d shapenets: A deep representation for volumetric shapes publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – year: 2008 ident: ref1 publication-title: Finite Element Method – volume: abs 1412 6980 year: 2014 ident: ref81 article-title: Adam: A method for stochastic optimization publication-title: CoRR – start-page: 1 year: 2015 ident: ref7 article-title: The visual object tracking vot2015 challenge results publication-title: Proc IEEE Int Conf Comput Vis Workshops – ident: ref75 doi: 10.1145/2461912.2461931 – start-page: 178 year: 1986 ident: ref15 article-title: Learning while searching in constraint-satisfaction problems publication-title: Proc AAAI Nat Conf Artificial Intell – ident: ref26 doi: 10.1145/2343483.2343495 – ident: ref60 doi: 10.1145/2816795.2818063 – ident: ref9 doi: 10.1007/BF02551274 – ident: ref42 doi: 10.1145/3072959.3073631 – ident: ref74 doi: 10.1145/2766938 – ident: ref44 doi: 10.1145/2897824.2925979 – ident: ref58 doi: 10.1145/1731047.1731054 – start-page: 806 year: 2014 ident: ref16 article-title: Cnn features off-the-shelf: An astounding baseline for recognition publication-title: Proc IEEE Conf Comp Vis Pattern Recognit – ident: ref63 doi: 10.1145/74333.74355 – ident: ref11 doi: 10.1145/2980179.2980236 – ident: ref68 doi: 10.1145/2816795.2818090 – ident: ref25 doi: 10.1007/s11263-015-0816-y – ident: ref53 doi: 10.1145/2508363.2508406 – ident: ref78 doi: 10.1145/3072959.3073655 – ident: ref37 doi: 10.1038/nature14236 – ident: ref12 doi: 10.1145/545261.545269 – ident: ref62 doi: 10.1145/2980179.2982437 – ident: ref69 doi: 10.1145/2508363.2508392 – ident: ref32 doi: 10.1145/2601097.2601136 – ident: ref65 doi: 10.1145/2816795.2818089 – ident: ref71 doi: 10.1145/2601097.2601217 – ident: ref38 doi: 10.1145/3083723 – ident: ref45 doi: 10.1145/1409060.1409118 – ident: ref40 doi: 10.1145/3072959.3073663 – ident: ref28 doi: 10.1145/2010324.1964988 – ident: ref54 doi: 10.1145/2343483.2343501 – volume: abs 1606 915 year: 2016 ident: ref22 article-title: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS publication-title: CoRR – ident: ref8 doi: 10.1016/0893-6080(91)90009-T – ident: ref61 doi: 10.1145/2990496 – ident: ref85 doi: 10.1145/2766904 – ident: ref41 doi: 10.1145/2766911 – ident: ref34 doi: 10.1145/1882261.1866160 – ident: ref51 doi: 10.1145/1778765.1778776 – start-page: 1097 year: 2012 ident: ref18 article-title: Imagenet classification with deep convolutional neural networks publication-title: Proc Advances Neural Inf Process Syst – volume: 36 year: 2017 ident: ref39 article-title: Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning publication-title: ACM Trans Graph doi: 10.1145/3072959.3073602 – ident: ref14 doi: 10.1145/1964921.1964986 – volume: 35 year: 2016 ident: ref36 article-title: Guided learning of control graphs for physics-based characters publication-title: ACM Trans Graph doi: 10.1145/2893476 – ident: ref4 doi: 10.1109/TASL.2011.2134090 – volume: 99 year: 1999 ident: ref52 article-title: Interactive animation of structured deformable objects publication-title: Graph Interface – ident: ref49 doi: 10.1145/1073204.1073216 – ident: ref29 doi: 10.1145/2010324.1964966 – ident: ref56 doi: 10.1145/1028523.1028541 – ident: ref57 doi: 10.1145/2231816.2231821 – ident: ref77 doi: 10.1145/2461912.2461961 – ident: ref84 doi: 10.1109/38.20317 – ident: ref3 doi: 10.1109/ICASSP.2013.6639081 – ident: ref24 doi: 10.1109/TPAMI.2015.2437384 |
| SSID | ssj0014489 |
| Score | 2.4781616 |
| Snippet | NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1745 |
| SubjectTerms | Animation Artificial neural networks Computational modeling Computer simulation Constitutive models data-driven animation deformable model Deformable models Deformation Displacement Elasticity Finite element method Formability Machine learning Mathematical analysis Mathematical models Matrix methods Neural network Neural networks nonlinear regression Object recognition Parameterization physics-based simulation Simulation Strain Substructuring Three dimensional models Training |
| Title | NNWarp: Neural Network-Based Nonlinear Deformation |
| URI | https://ieeexplore.ieee.org/document/8536417 https://www.ncbi.nlm.nih.gov/pubmed/30442607 https://www.proquest.com/docview/2372304876 https://www.proquest.com/docview/2135131981 |
| Volume | 26 |
| WOSCitedRecordID | wos000519547200010&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 | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0506 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014489 issn: 1077-2626 databaseCode: RIE dateStart: 19950101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED-2IaIPfs2P6RwVfBKztWnWpL7pdPogxYcx91bSJAVBttFt_v1e2q4oqOBboZe0XC65u9zd7wAujSspahJBNOUJYUwZkqCVTSRKdCK1r0TeeW78zKNITCbhSw2uq1oYY0yefGa69jGP5euZWtmrsh6qloB5vA51zoOiVquKGKCbERb5hZxQtNLLCKbnhr3RePBok7hElwphO9NuwSZ68RabnX9TR3l_ld9NzVzlDHf_97N7sFOals5tIQv7UDPTA9j-AjjYBBpFrzKb3zgWkwNpoyIJnNyhLtNOVMBmyMy5N1VR4yGMhg-jwRMpuyYQ5bNwSVLNJHVTzblkinkiYKHPEumrRPW5TxNXBXg-90ORonHmpcpL-tpI3NuJNiw0_hE0prOpOQFHKtdIyT3TRx1mI3oWukd7TAfSRzssbYG75l2sSkRx29jiPc49CzeMLedjy_m45HwLrqoh8wJO4y_ipmVrRVhytAXt9QLF5YZbxNTn9nobz_YWXFSvcavY-IecmtkKaWw3QjxyBM58XCxsNfdaHk5__uYZbFHraOcpO21oLLOVOYcN9bF8W2QdlMeJ6OTy-AkkSdcn |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLZgIB4H3o_yLBInRFibpkvKjTeIUXGYYLcqTVIJCW1Tt_H7cdquAgmQuFWqk1aOE9ux_Rng2HiSoiYRRFOeEsaUISla2USiRKdSB0oUnede2jyORbcbPU_BaV0LY4wpks_MmX0sYvm6r8b2qqyJqqXFfD4NMyFj1CurteqYAToaUZlhyAlFO72KYfpe1Oy8XN3ZNC5xRoWwvWkXYA79eIvOzr8ppKLDyu_GZqF0bpf_97srsFQZl-5FKQ2rMGV6a7D4BXJwHWgcv8p8cO5aVA6kjcs0cHKJ2ky7cQmcIXP32tRljRvQub3pXN2Tqm8CUQGLRiTTTFIv05xLppgvWiwKWCoDlaqQBzT1VAtP6DASGZpnfqb8NNRG4u5OtWGRCTah0ev3zDa4UnlGSu6bELWYjelZ8B7tM92SAVpimQPehHeJqjDFbWuL96TwLbwosZxPLOeTivMOnNRDBiWgxl_E65atNWHFUQf2JguUVFtumNCA2wtuPN0dOKpf42axERDZM_0x0th-hHjoCJx5q1zYeu6JPOz8_M1DmL_vPLWT9kP8uAsL1LrdRQLPHjRG-djsw6z6GL0N84NCKj8BbujZhg |
| 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=NNWarp%3A+Neural+Network-Based+Nonlinear+Deformation&rft.jtitle=IEEE+transactions+on+visualization+and+computer+graphics&rft.au=Luo%2C+Ran&rft.au=Shao%2C+Tianjia&rft.au=Wang%2C+Huamin&rft.au=Xu%2C+Weiwei&rft.date=2020-04-01&rft.issn=1941-0506&rft.eissn=1941-0506&rft.volume=26&rft.issue=4&rft.spage=1745&rft_id=info:doi/10.1109%2FTVCG.2018.2881451&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1077-2626&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1077-2626&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1077-2626&client=summon |