{"id":"https://openalex.org/W4415084754","doi":"https://doi.org/10.48550/arxiv.2503.01448","title":"Generative Human Geometry Distribution","display_name":"Generative Human Geometry Distribution","publication_year":2025,"publication_date":"2025-03-03","ids":{"openalex":"https://openalex.org/W4415084754","doi":"https://doi.org/10.48550/arxiv.2503.01448"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2503.01448","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.01448","pdf_url":"https://arxiv.org/pdf/2503.01448","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2503.01448","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087452083","display_name":"Xiangjun Tang","orcid":"https://orcid.org/0000-0001-7441-0086"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tang, Xiangjun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100363706","display_name":"Biao Zhang","orcid":"https://orcid.org/0000-0001-5685-6092"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Biao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5076768552","display_name":"Peter Wonka","orcid":"https://orcid.org/0000-0003-0627-9746"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wonka, Peter","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087452083"],"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/T12006","display_name":"Ergonomics and Musculoskeletal Disorders","score":0.6940000057220459,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12006","display_name":"Ergonomics and Musculoskeletal Disorders","score":0.6940000057220459,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6531999707221985,"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/T13113","display_name":"Engineering Technology and Methodologies","score":0.6279000043869019,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/feature","display_name":"Feature (linguistics)","score":0.4702000021934509},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45249998569488525},{"id":"https://openalex.org/keywords/complex-geometry","display_name":"Complex geometry","score":0.45170000195503235},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4332999885082245},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4309999942779541},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.42640000581741333},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.41679999232292175},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.41449999809265137},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4023999869823456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5685999989509583},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.5088000297546387},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45249998569488525},{"id":"https://openalex.org/C158843486","wikidata":"https://www.wikidata.org/wiki/Q2137810","display_name":"Complex geometry","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4465000033378601},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.42640000581741333},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C146318809","wikidata":"https://www.wikidata.org/wiki/Q3398332","display_name":"Hand geometry","level":3,"score":0.3952000141143799},{"id":"https://openalex.org/C109546454","wikidata":"https://www.wikidata.org/wiki/Q3798604","display_name":"Information geometry","level":4,"score":0.3855000138282776},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.38119998574256897},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3402999937534332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32600000500679016},{"id":"https://openalex.org/C2779521785","wikidata":"https://www.wikidata.org/wiki/Q5535529","display_name":"Geometry processing","level":3,"score":0.32580000162124634},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32199999690055847},{"id":"https://openalex.org/C31243852","wikidata":"https://www.wikidata.org/wiki/Q1666739","display_name":"Stochastic geometry","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C192939610","wikidata":"https://www.wikidata.org/wiki/Q188444","display_name":"Differential geometry","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.29190000891685486},{"id":"https://openalex.org/C104065381","wikidata":"https://www.wikidata.org/wiki/Q1002535","display_name":"Geometric modeling","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C50637493","wikidata":"https://www.wikidata.org/wiki/Q1136781","display_name":"Morphing","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C32990609","wikidata":"https://www.wikidata.org/wiki/Q306542","display_name":"Transformation geometry","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C29123130","wikidata":"https://www.wikidata.org/wiki/Q874709","display_name":"Computational geometry","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2503.01448","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.01448","pdf_url":"https://arxiv.org/pdf/2503.01448","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2503.01448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.01448","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2503.01448","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.01448","pdf_url":"https://arxiv.org/pdf/2503.01448","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Realistic":[0],"human":[1,43],"geometry":[2,44,75,137,191],"generation":[3,170],"is":[4,61],"an":[5],"important":[6],"yet":[7],"challenging":[8],"task,":[9],"requiring":[10],"both":[11],"the":[12,19,99,106,132,148],"preservation":[13],"of":[14,22,102],"fine":[15],"clothing":[16],"details":[17],"and":[18,63,93,104,127,171],"accurate":[20],"modeling":[21],"clothing-body":[23],"interactions.":[24],"To":[25,68],"tackle":[26],"this":[27,156],"challenge,":[28],"we":[29,71,135],"build":[30],"upon":[31],"Geometry":[32],"distributions,":[33],"a":[34,41,49,55,59,73,114,118,140,144,187],"recently":[35],"proposed":[36],"representation":[37],"that":[38,179],"can":[39],"model":[40,77,154],"single":[42],"with":[45],"high":[46],"fidelity":[47],"using":[48,95,143],"flow":[50,108,146,153],"matching":[51],"model.":[52],"However,":[53],"extending":[54],"single-geometry":[56],"distribution":[57,76],"to":[58,124],"dataset":[60],"non-trivial":[62],"inefficient":[64],"for":[65],"large-scale":[66],"learning.":[67],"address":[69],"this,":[70],"propose":[72],"new":[74],"by":[78],"two":[79,119,164],"key":[80,165],"techniques:":[81],"(1)":[82],"encoding":[83],"distributions":[84,138],"as":[85,98],"2D":[86],"feature":[87],"maps":[88],"rather":[89],"than":[90],"network":[91],"parameters,":[92],"(2)":[94],"SMPL":[96],"models":[97],"domain":[100],"instead":[101],"Gaussian":[103],"refining":[105],"associated":[107],"velocity":[109],"field.":[110],"We":[111,159],"then":[112],"design":[113],"generative":[115,129],"framework":[116],"adopting":[117],"staged":[120],"training":[121],"paradigm":[122],"analogous":[123],"state-of-the-art":[125,184],"image":[126],"3D":[128],"models.":[130],"In":[131],"first":[133],"stage,":[134],"compress":[136],"into":[139],"latent":[141,157],"space":[142],"diffusion":[145],"model;":[147],"second":[149],"stage":[150],"trains":[151],"another":[152],"on":[155,163],"space.":[158],"validate":[160],"our":[161,180],"approach":[162],"tasks:":[166],"pose-conditioned":[167],"random":[168],"avatar":[169],"avatar-consistent":[172],"novel":[173],"pose":[174],"synthesis.":[175],"Experimental":[176],"results":[177],"demonstrate":[178],"method":[181],"outperforms":[182],"existing":[183],"methods,":[185],"achieving":[186],"57%":[188],"improvement":[189],"in":[190],"quality.":[192]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-12T00:00:00"}
