{"id":"https://openalex.org/W7136456614","doi":"https://doi.org/10.48550/arxiv.2603.12789","title":"Coherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass","display_name":"Coherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7136456614","doi":"https://doi.org/10.48550/arxiv.2603.12789"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12789","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12789","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.12789","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129425190","display_name":"Sangmin Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Sangmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082652288","display_name":"Minhyuk Hwang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hwang, Minhyuk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051789113","display_name":"Geonho Cha","orcid":"https://orcid.org/0000-0002-3008-4642"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cha, Geonho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028451951","display_name":"Dongyoon Wee","orcid":"https://orcid.org/0000-0003-0359-146X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wee, Dongyoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129398047","display_name":"Jaesik Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Jaesik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.9472000002861023,"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":0.9472000002861023,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.008700000122189522,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.008200000040233135,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5949000120162964},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.546500027179718},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.491100013256073},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.47999998927116394},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4616999924182892},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.45660001039505005},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4537000060081482},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.40450000762939453},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3953000009059906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7572000026702881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6859999895095825},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6353999972343445},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5949000120162964},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.491100013256073},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.45660001039505005},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4537000060081482},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3382999897003174},{"id":"https://openalex.org/C31487907","wikidata":"https://www.wikidata.org/wiki/Q1154597","display_name":"Polygon mesh","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2702000141143799},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C2986578859","wikidata":"https://www.wikidata.org/wiki/Q657632","display_name":"Human motion","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C2777708103","wikidata":"https://www.wikidata.org/wiki/Q852589","display_name":"Motion blur","level":3,"score":0.25459998846054077},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12789","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12789","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.12789","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12789","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":"Preprint"},"sustainable_development_goals":[{"score":0.40522345900535583,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,11,148],"3D":[3],"foundation":[4],"models":[5],"have":[6],"led":[7],"to":[8,29,93,110],"growing":[9],"interest":[10],"reconstructing":[12],"humans":[13,99],"and":[14,26,55,73,78,87,100,140,152],"their":[15],"surrounding":[16],"environments.":[17],"However,":[18],"most":[19],"existing":[20],"approaches":[21],"focus":[22],"on":[23,64,136],"monocular":[24],"inputs,":[25],"extending":[27],"them":[28],"multi-view":[30,60,107,153,164],"settings":[31],"requires":[32],"additional":[33],"overhead":[34],"modules":[35,66],"or":[36,67],"preprocessed":[37],"data.":[38],"To":[39],"this":[40],"end,":[41],"we":[42,121],"present":[43],"CHROMM,":[44],"a":[45,81,89,106,115,123],"unified":[46],"framework":[47],"that":[48,143],"jointly":[49],"estimates":[50,113],"cameras,":[51],"scene":[52],"point":[53],"clouds,":[54],"human":[56,74,150],"meshes":[57],"from":[58,76],"multi-person":[59,125],"videos":[61],"without":[62],"relying":[63],"external":[65],"preprocessing.":[68],"We":[69,103],"integrate":[70],"strong":[71],"geometric":[72],"priors":[75],"Pi3X":[77],"Multi-HMR":[79],"into":[80,114],"single":[82,116],"trainable":[83],"neural":[84],"network":[85],"architecture,":[86],"introduce":[88,105],"scale":[90,96],"adjustment":[91],"module":[92],"solve":[94],"the":[95,101],"discrepancy":[97],"between":[98],"scene.":[102],"also":[104],"fusion":[108],"strategy":[109],"aggregate":[111],"per-view":[112],"representation":[117],"at":[118],"test-time.":[119],"Finally,":[120],"propose":[122],"geometry-based":[124],"association":[126],"method,":[127],"which":[128],"is":[129],"more":[130],"robust":[131],"than":[132,161],"appearance-based":[133],"approaches.":[134,165],"Experiments":[135],"EMDB,":[137],"RICH,":[138],"EgoHumans,":[139],"EgoExo4D":[141],"show":[142],"CHROMM":[144],"achieves":[145],"competitive":[146],"performance":[147],"global":[149],"motion":[151],"pose":[154],"estimation":[155],"while":[156],"running":[157],"over":[158],"8x":[159],"faster":[160],"prior":[162],"optimization-based":[163],"Project":[166],"page:":[167],"https://nstar1125.github.io/chromm.":[168]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-17T00:00:00"}
