{"id":"https://openalex.org/W7159056330","doi":"https://doi.org/10.48550/arxiv.2604.26620","title":"SnapPose3D: Diffusion-Based Single-Frame 2D-to-3D Lifting of Human Poses","display_name":"SnapPose3D: Diffusion-Based Single-Frame 2D-to-3D Lifting of Human Poses","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7159056330","doi":"https://doi.org/10.48550/arxiv.2604.26620"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.26620","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26620","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.26620","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134890127","display_name":"Alessandro Simoni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simoni, Alessandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121077279","display_name":"Riccardo Catalini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Catalini, Riccardo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092543610","display_name":"Davide Di Nucci","orcid":"https://orcid.org/0009-0000-7450-8796"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Nucci, Davide","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054837437","display_name":"Guido Borghi","orcid":"https://orcid.org/0000-0003-2441-7524"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Borghi, Guido","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093427267","display_name":"Davide Davoli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Davoli, Davide","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014520592","display_name":"Lorenzo Garattoni","orcid":"https://orcid.org/0000-0002-6382-0335"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garattoni, Lorenzo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016401744","display_name":"Gianpiero Francesca","orcid":"https://orcid.org/0000-0001-6066-9345"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francesca, Gianpiero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080553734","display_name":"Yuki Kawana","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kawana, Yuki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081341599","display_name":"Roberto Vezzani","orcid":"https://orcid.org/0000-0002-1046-6870"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vezzani, Roberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.8826000094413757,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.8826000094413757,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.049800001084804535,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12290","display_name":"Human Motion and Animation","score":0.030500000342726707,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/ambiguity","display_name":"Ambiguity","score":0.7146999835968018},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5914000272750854},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5536999702453613},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5408999919891357},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5210999846458435},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.39070001244544983},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3785000145435333},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3481000065803528},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.3458000123500824}],"concepts":[{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.7146999835968018},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6938999891281128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6826000213623047},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5914000272750854},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5536999702453613},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4697999954223633},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4408999979496002},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3785000145435333},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.34130001068115234},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30379998683929443},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.29109999537467957},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.25609999895095825},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.26620","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26620","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.26620","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26620","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Depth":[0],"ambiguity":[1,116],"and":[2,61,87,129,136,162],"joint":[3,32],"uncertainty":[4],"are":[5,28],"the":[6,22,137,151],"two":[7],"main":[8],"obstacles":[9],"in":[10,21,64],"obtaining":[11],"accurate":[12,67,164],"human":[13,153],"pose":[14,89,115,154],"predictions":[15],"by":[16,30,117],"2D-to-3D":[17],"lifting":[18],"methods":[19,112],"proposed":[20],"literature.":[23],"In":[24],"particular,":[25],"these":[26,48],"issues":[27],"caused":[29],"2D":[31,88],"locations":[33],"that":[34,113,166],"can":[35],"be":[36],"mapped":[37],"to":[38,57,78,160,168],"multiple":[39,43,59,99],"3D":[40,80,152],"positions,":[41],"inducing":[42],"possible":[44],"final":[45,66],"poses.":[46],"Following":[47],"considerations,":[49],"we":[50,70],"propose":[51],"leveraging":[52],"diffusion-based":[53],"models":[54],"generation":[55],"capability":[56],"predict":[58],"hypotheses":[60,100,165],"aggregate":[62,163],"them":[63],"a":[65,73,93,105],"pose.":[68],"Therefore,":[69],"introduce":[71],"SnapPose3D,":[72],"pose-lifting":[74],"framework":[75],"trained":[76],"deterministically":[77],"denoise":[79],"poses":[81],"conditioned":[82],"on":[83,147],"both":[84],"visual":[85],"context":[86],"features.":[90],"SnapPose3D":[91,121,146],"adopts":[92],"probabilistic":[94],"approach":[95],"during":[96],"inference,":[97],"generating":[98],"through":[101],"random":[102],"sampling":[103],"from":[104],"unit":[106],"Gaussian":[107],"distribution.":[108],"Unlike":[109],"most":[110],"previous":[111],"address":[114],"processing":[118],"temporal":[119],"sequences,":[120],"uses":[122],"single":[123],"frames":[124],"as":[125],"input,":[126],"avoiding":[127],"tracking":[128],"limiting":[130],"computational":[131],"cost,":[132],"data":[133],"acquisition":[134],"complexity,":[135],"need":[138],"for":[139,150],"online,":[140],"real-time":[141],"applications.":[142],"We":[143],"extensively":[144],"evaluate":[145],"well-known":[148],"benchmarks":[149],"estimation":[155],"task":[156],"showing":[157],"its":[158],"ability":[159],"generate":[161],"lead":[167],"state-of-the-art":[169],"results.":[170]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-01T00:00:00"}