{"id":"https://openalex.org/W7155532596","doi":"https://doi.org/10.48550/arxiv.2604.21575","title":"OmniFit: Multi-modal 3D Body Fitting via Scale-agnostic Dense Landmark Prediction","display_name":"OmniFit: Multi-modal 3D Body Fitting via Scale-agnostic Dense Landmark Prediction","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155532596","doi":"https://doi.org/10.48550/arxiv.2604.21575"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.21575","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21575","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.2604.21575","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134554403","display_name":"Zeyu Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Zeyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046118420","display_name":"Yuliang Xiu","orcid":"https://orcid.org/0000-0003-0165-5909"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiu, Yuliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134516117","display_name":"Renke Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Renke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084778993","display_name":"Zhijing Shao","orcid":"https://orcid.org/0009-0008-3204-3271"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Zhijing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114138485","display_name":"Xiaoben Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaoben","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134468928","display_name":"Siyuan Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Siyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134480629","display_name":"Chao Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134555895","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-6011-8081"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134510082","display_name":"Baigui Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Baigui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134539698","display_name":"Jian Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134474095","display_name":"Zhenyu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhenyu","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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.7800999879837036,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.7800999879837036,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.1412999927997589,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.024700000882148743,"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/metric","display_name":"Metric (unit)","score":0.4456999897956848},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4293999969959259},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3950999975204468},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.39480000734329224},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3711000084877014},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3328000009059906},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3273000121116638},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.32359999418258667},{"id":"https://openalex.org/keywords/body-shape","display_name":"Body shape","score":0.32330000400543213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711000204086304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6139000058174133},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5460000038146973},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4456999897956848},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4293999969959259},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.39480000734329224},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36160001158714294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C60627051","wikidata":"https://www.wikidata.org/wiki/Q1072278","display_name":"Body shape","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C3019835501","wikidata":"https://www.wikidata.org/wiki/Q1310130","display_name":"Single shot","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.28610000014305115},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C3020541326","wikidata":"https://www.wikidata.org/wiki/Q1796666","display_name":"Body surface","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26429998874664307},{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.21575","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21575","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.2604.21575","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21575","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":[{"id":"https://metadata.un.org/sdg/9","score":0.6056591868400574,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fitting":[0],"an":[1,110],"underlying":[2],"body":[3,98,136,164],"model":[4],"to":[5,96,118,134,145,167,175],"3D":[6],"clothed":[7],"human":[8],"assets":[9,44],"has":[10],"been":[11],"extensively":[12],"studied,":[13],"yet":[14,86],"most":[15],"approaches":[16],"focus":[17],"on":[18,179],"either":[19],"single-modal":[20],"inputs":[21],"such":[22],"as":[23],"point":[24],"clouds":[25],"or":[26],"multi-view":[27,169],"images":[28],"alone,":[29],"often":[30],"requiring":[31],"a":[32,53,84,127],"known":[33],"metric":[34],"scale.":[35],"This":[36],"constraint":[37],"is":[38,48,83,161],"frequently":[39],"impractical,":[40],"especially":[41],"for":[42,74,104,120],"AI-generated":[43],"where":[45],"scale":[46,129],"distortion":[47],"common.":[49],"We":[50,124],"propose":[51],"OmniFit,":[52],"method":[54,166],"that":[55,91,131],"can":[56],"seamlessly":[57],"handle":[58],"diverse":[59],"multi-modal":[60],"inputs,":[61],"including":[62],"full":[63],"scans,":[64],"partial":[65],"depth":[66],"observations,":[67],"and":[68,77,150,172,182],"image":[69,113],"captures,":[70],"while":[71],"remaining":[72],"scale-agnostic":[73],"both":[75],"real":[76],"synthetic":[78],"assets.":[79],"Our":[80],"key":[81],"innovation":[82],"simple":[85],"effective":[87],"conditional":[88],"transformer":[89],"decoder":[90],"directly":[92],"maps":[93],"surface":[94],"points":[95],"dense":[97],"landmarks,":[99],"which":[100],"are":[101],"then":[102],"used":[103],"SMPL-X":[105],"parameter":[106],"fitting.":[107],"In":[108],"addition,":[109],"optional":[111],"plug-and-play":[112],"adapter":[114],"incorporates":[115],"visual":[116],"cues":[117],"compensate":[119],"missing":[121],"geometric":[122],"information.":[123],"further":[125],"introduce":[126],"dedicated":[128],"predictor":[130],"rescales":[132],"subjects":[133],"canonical":[135],"proportions.":[137],"OmniFit":[138],"substantially":[139],"outperforms":[140],"state-of-the-art":[141],"methods":[142],"by":[143],"57.1":[144],"80.9":[146],"percent":[147],"across":[148],"daily":[149],"loose":[151],"clothing":[152],"scenarios.":[153],"To":[154],"the":[155,162,173,180],"best":[156],"of":[157],"our":[158],"knowledge,":[159],"it":[160],"first":[163,174],"fitting":[165],"surpass":[168],"optimization":[170],"baselines":[171],"achieve":[176],"millimeter-level":[177],"accuracy":[178],"CAPE":[181],"4D-DRESS":[183],"benchmarks.":[184]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-25T00:00:00"}