{"id":"https://openalex.org/W4313166292","doi":"https://doi.org/10.1109/cvpr52688.2022.02015","title":"SMPL-A: Modeling Person-Specific Deformable Anatomy","display_name":"SMPL-A: Modeling Person-Specific Deformable Anatomy","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4313166292","doi":"https://doi.org/10.1109/cvpr52688.2022.02015"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52688.2022.02015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52688.2022.02015","pdf_url":null,"source":{"id":"https://openalex.org/S4363607701","display_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030891246","display_name":"Hengtao Guo","orcid":"https://orcid.org/0000-0002-4734-026X"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hengtao Guo","raw_affiliation_strings":["United Imaging Intelligence,Cambridge,MA,USA","United Imaging Intelligence, Cambridge, MA, USA","Rensselaer Polytechnic Institute, Troy, NY, USA"],"affiliations":[{"raw_affiliation_string":"United Imaging Intelligence,Cambridge,MA,USA","institution_ids":[]},{"raw_affiliation_string":"United Imaging Intelligence, Cambridge, MA, USA","institution_ids":[]},{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068780385","display_name":"Benjamin Planche","orcid":"https://orcid.org/0000-0002-6110-6437"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Benjamin Planche","raw_affiliation_strings":["United Imaging Intelligence,Cambridge,MA,USA","United Imaging Intelligence, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"United Imaging Intelligence,Cambridge,MA,USA","institution_ids":[]},{"raw_affiliation_string":"United Imaging Intelligence, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038942290","display_name":"Meng Zheng","orcid":"https://orcid.org/0000-0002-6677-2017"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng Zheng","raw_affiliation_strings":["United Imaging Intelligence,Cambridge,MA,USA","United Imaging Intelligence, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"United Imaging Intelligence,Cambridge,MA,USA","institution_ids":[]},{"raw_affiliation_string":"United Imaging Intelligence, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044410531","display_name":"Srikrishna Karanam","orcid":"https://orcid.org/0000-0002-7627-7765"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srikrishna Karanam","raw_affiliation_strings":["United Imaging Intelligence,Cambridge,MA,USA","United Imaging Intelligence, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"United Imaging Intelligence,Cambridge,MA,USA","institution_ids":[]},{"raw_affiliation_string":"United Imaging Intelligence, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038526207","display_name":"Terrence Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Terrence Chen","raw_affiliation_strings":["United Imaging Intelligence,Cambridge,MA,USA","United Imaging Intelligence, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"United Imaging Intelligence,Cambridge,MA,USA","institution_ids":[]},{"raw_affiliation_string":"United Imaging Intelligence, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003798053","display_name":"Ziyan Wu","orcid":"https://orcid.org/0000-0002-9774-7770"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ziyan Wu","raw_affiliation_strings":["United Imaging Intelligence,Cambridge,MA,USA","United Imaging Intelligence, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"United Imaging Intelligence,Cambridge,MA,USA","institution_ids":[]},{"raw_affiliation_string":"United Imaging Intelligence, Cambridge, MA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5030891246"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":2.8854,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.93195534,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"20782","last_page":"20791"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11984","display_name":"Anatomy and Medical Technology","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11984","display_name":"Anatomy and Medical Technology","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6994056105613708},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6886935830116272},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.572503387928009},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5011565685272217},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.4599854350090027},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3797287344932556},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3598240315914154},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2410583794116974}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6994056105613708},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6886935830116272},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.572503387928009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5011565685272217},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.4599854350090027},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3797287344932556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3598240315914154},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2410583794116974}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr52688.2022.02015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52688.2022.02015","pdf_url":null,"source":{"id":"https://openalex.org/S4363607701","display_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1484334610","https://openalex.org/W1490630562","https://openalex.org/W1522301498","https://openalex.org/W1967554269","https://openalex.org/W2017950984","https://openalex.org/W2052882206","https://openalex.org/W2131219466","https://openalex.org/W2133287637","https://openalex.org/W2137044594","https://openalex.org/W2361141013","https://openalex.org/W2483862638","https://openalex.org/W2560609797","https://openalex.org/W2573098616","https://openalex.org/W2768683308","https://openalex.org/W2769095470","https://openalex.org/W2798637590","https://openalex.org/W2803591142","https://openalex.org/W2883952443","https://openalex.org/W2891631795","https://openalex.org/W2926629410","https://openalex.org/W2929444146","https://openalex.org/W2962729173","https://openalex.org/W2962941647","https://openalex.org/W2963847924","https://openalex.org/W2977796156","https://openalex.org/W2978956737","https://openalex.org/W3021998000","https://openalex.org/W3096095847","https://openalex.org/W3101022589","https://openalex.org/W3107393033","https://openalex.org/W3135301258","https://openalex.org/W3171097378","https://openalex.org/W3189212558","https://openalex.org/W3199360076","https://openalex.org/W4236667477","https://openalex.org/W4286981811","https://openalex.org/W4295312788","https://openalex.org/W6631190155","https://openalex.org/W6679440167","https://openalex.org/W6680284190","https://openalex.org/W6722076910","https://openalex.org/W6761022565","https://openalex.org/W6763422710","https://openalex.org/W6766978945","https://openalex.org/W6781405854","https://openalex.org/W6791205910","https://openalex.org/W6800780028"],"related_works":["https://openalex.org/W4389574804","https://openalex.org/W3016928466","https://openalex.org/W2936725271","https://openalex.org/W3150655618","https://openalex.org/W3108295644","https://openalex.org/W1578717197","https://openalex.org/W2626737336","https://openalex.org/W2005998065","https://openalex.org/W2980582925","https://openalex.org/W3126423817"],"abstract_inverted_index":{"A":[0],"variety":[1],"of":[2,53,105],"diagnostic":[3],"and":[4,25,63,80,93,97,135,157,169,251],"therapeutic":[5],"protocols":[6],"rely":[7],"on":[8,194,225],"locating":[9],"in":[10,50,70,129],"vivo":[11],"target":[12,38],"anatomical":[13,252],"structures,":[14],"which":[15,198],"can":[16,180,216,235],"be":[17,217,236],"obtained":[18],"from":[19,174],"medical":[20,137,144,176,258],"scans.":[21],"However,":[22],"organs":[23],"move":[24],"deform":[26],"as":[27],"the":[28,71,91,100,103,111,120,154,163,170,226,245,257],"patient":[29],"changes":[30],"his/her":[31],"pose.":[32],"In":[33],"order":[34,130],"to":[35,43,55,67,99,113,118,131,146,186,189],"obtain":[36],"accurate":[37],"location":[39],"information,":[40],"clinicians":[41,92],"have":[42],"either":[44],"conduct":[45,192],"frequent":[46],"intraoperative":[47],"scans,":[48,177],"resulting":[49],"higher":[51],"exposition":[52],"patients":[54,69],"radiations,":[56],"or":[57],"adopt":[58],"proxy":[59,85],"procedures":[60],"(e.g.,":[61],"creating":[62],"using":[64,205],"custom":[65,84],"molds":[66],"keep":[68],"exact":[72],"same":[73],"pose":[74,167,228],"during":[75],"both":[76],"preoperative":[77],"organ":[78,123,184,214],"scanning":[79],"subsequent":[81],"treatment.":[82],"Such":[83],"methods":[86],"are":[87],"typically":[88],"sub-optimal,":[89],"constraining":[90],"costing":[94],"precious":[95],"time":[96],"money":[98],"patients.":[101],"To":[102],"best":[104],"our":[106,178],"knowledge,":[107],"this":[108,233],"work":[109,234],"is":[110,199],"first":[112,142],"present":[114],"a":[115,148,195,220,237],"learning-based":[116],"approach":[117],"estimate":[119,181],"patient's":[121,164],"internal":[122],"deformation":[124,185,215],"for":[125,240],"arbitrary":[126],"human":[127,248],"poses":[128],"assist":[132],"with":[133,254],"radiotherapy":[134],"similar":[136],"protocols.":[138],"The":[139],"underlying":[140],"method":[141,179],"leverages":[143],"scans":[145],"learn":[147],"patient-specific":[149],"representation":[150,172],"that":[151,212,232],"potentially":[152],"encodes":[153],"organ's":[155,171],"shape":[156],"elastic":[158],"properties.":[159],"During":[160],"inference,":[161],"given":[162],"current":[165,183],"body":[166],"information":[168],"extracted":[173],"previous":[175],"their":[182],"offer":[187],"guidance":[188],"clinicians.":[190],"We":[191,230],"experiments":[193],"well-sized":[196],"dataset":[197],"augmented":[200],"through":[201,219],"real":[202],"clinical":[203],"data":[204],"finite":[206],"element":[207],"modeling.":[208],"Our":[209],"results":[210],"suggest":[211],"pose-dependent":[213],"learned":[218],"point":[221,239],"cloud":[222],"autoencoder":[223],"conditioned":[224],"parametric":[227],"input.":[229],"hope":[231],"starting":[238],"future":[241],"research":[242],"towards":[243],"closing":[244],"loop":[246],"between":[247],"mesh":[249],"recovery":[250],"reconstruction,":[253],"applications":[255],"beyond":[256],"domain.":[259]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
