{"id":"https://openalex.org/W4416531062","doi":"https://doi.org/10.1109/iccv51701.2025.00487","title":"SIGMAN: Scaling 3D Human Gaussian Generation with Millions of Assets","display_name":"SIGMAN: Scaling 3D Human Gaussian Generation with Millions of Assets","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416531062","doi":"https://doi.org/10.1109/iccv51701.2025.00487"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2504.06982","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101756750","display_name":"Yuhang Yang","orcid":"https://orcid.org/0000-0002-3135-5803"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuhang Yang","raw_affiliation_strings":["USTC,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"USTC,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002528934","display_name":"Fengqi Liu","orcid":"https://orcid.org/0000-0001-6101-2644"},"institutions":[{"id":"https://openalex.org/I4210091156","display_name":"SAIC-GM (China)","ror":"https://ror.org/00h64t852","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091156"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengqi Liu","raw_affiliation_strings":["Shanghai AI Lab,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai AI Lab,China","institution_ids":["https://openalex.org/I4210091156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051894171","display_name":"Yuanrong Lu","orcid":"https://orcid.org/0000-0002-2639-8112"},"institutions":[{"id":"https://openalex.org/I2802039673","display_name":"California Miramar University","ror":"https://ror.org/04phxq043","country_code":"US","type":"education","lineage":["https://openalex.org/I2802039673"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixing Lu","raw_affiliation_strings":["CMU,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMU,USA","institution_ids":["https://openalex.org/I2802039673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020392820","display_name":"Qin Zhao","orcid":"https://orcid.org/0000-0002-3519-9190"},"institutions":[{"id":"https://openalex.org/I4210091156","display_name":"SAIC-GM (China)","ror":"https://ror.org/00h64t852","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091156"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Zhao","raw_affiliation_strings":["Shanghai AI Lab,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai AI Lab,China","institution_ids":["https://openalex.org/I4210091156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109200512","display_name":"P.C. Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pingyu Wu","raw_affiliation_strings":["USTC,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"USTC,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei Zhai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Zhai","raw_affiliation_strings":["USTC,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"USTC,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066573999","display_name":"Ran Yi","orcid":"https://orcid.org/0000-0003-3266-3805"},"institutions":[{"id":"https://openalex.org/I4210117317","display_name":"Tianjin Open University","ror":"https://ror.org/02d19m529","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210117317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Yi","raw_affiliation_strings":["SJTU,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SJTU,China","institution_ids":["https://openalex.org/I4210117317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017796934","display_name":"Yang Cao","orcid":"https://orcid.org/0000-0001-7034-2792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Cao","raw_affiliation_strings":["USTC,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"USTC,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084218062","display_name":"Lizhuang Ma","orcid":"https://orcid.org/0000-0003-1653-4341"},"institutions":[{"id":"https://openalex.org/I4210117317","display_name":"Tianjin Open University","ror":"https://ror.org/02d19m529","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210117317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhuang Ma","raw_affiliation_strings":["SJTU,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SJTU,China","institution_ids":["https://openalex.org/I4210117317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003217535","display_name":"Zheng-Jun Zha","orcid":"https://orcid.org/0000-0003-2510-8993"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng-Jun Zha","raw_affiliation_strings":["USTC,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"USTC,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078249371","display_name":"J.Q. Dong","orcid":"https://orcid.org/0000-0002-6416-6910"},"institutions":[{"id":"https://openalex.org/I4210091156","display_name":"SAIC-GM (China)","ror":"https://ror.org/00h64t852","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091156"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junting Dong","raw_affiliation_strings":["Shanghai AI Lab,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai AI Lab,China","institution_ids":["https://openalex.org/I4210091156"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5122","last_page":"5133"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6330000162124634,"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":0.6330000162124634,"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":0.15530000627040863,"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":0.06539999693632126,"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/gaussian","display_name":"Gaussian","score":0.43810001015663147},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.43149998784065247},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.41670000553131104},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.39340001344680786},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3431999981403351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33799999952316284},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.31949999928474426},{"id":"https://openalex.org/keywords/clothing","display_name":"Clothing","score":0.3190999925136566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6922000050544739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5565000176429749},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.43149998784065247},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39070001244544983},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36880001425743103},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34279999136924744},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C530175646","wikidata":"https://www.wikidata.org/wiki/Q11460","display_name":"Clothing","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C2779308522","wikidata":"https://www.wikidata.org/wiki/Q843958","display_name":"Digitization","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.26170000433921814}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.00487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2504.06982","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.06982","pdf_url":"https://arxiv.org/pdf/2504.06982","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2504.06982","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2504.06982","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":"pmh:oai:arXiv.org:2504.06982","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.06982","pdf_url":"https://arxiv.org/pdf/2504.06982","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2694841154","display_name":null,"funder_award_id":"62306295,62225207,62436008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416531062.pdf","grobid_xml":"https://content.openalex.org/works/W4416531062.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"3D":[0,18,37,86,107,164,184],"human":[1,38,87,108,185],"digitization":[2],"has":[3],"long":[4],"been":[5],"a":[6,101,118,134],"highly":[7],"pursued":[8],"yet":[9],"challenging":[10],"task.":[11],"Existing":[12],"methods":[13],"aim":[14],"to":[15,75,79,155,167],"generate":[16],"high-quality":[17,183],"digital":[19],"humans":[20],"from":[21],"single":[22],"or":[23],"multiple":[24],"views,":[25],"but":[26],"remain":[27,89],"primarily":[28],"constrained":[29],"by":[30,62,179],"current":[31],"paradigms":[32],"and":[33,48,53,69,81,192],"the":[34,128,147,157,169],"scarcity":[35],"of":[36],"assets.":[39],"Specifically,":[40],"recent":[41],"approaches":[42],"fall":[43],"into":[44,115,133],"several":[45],"paradigms:":[46],"optimization-based":[47],"feed-forward":[49],"(both":[50],"single-view":[51],"regression":[52],"multi-view":[54,113,148],"generation":[55,104],"with":[56,122,152,187],"reconstruction).":[57],"However,":[58],"they":[59],"are":[60],"limited":[61],"slow":[63],"speed,":[64],"low":[65],"quality,":[66],"cascade":[67],"reasoning,":[68],"ambiguity":[70],"in":[71],"mapping":[72,131],"low-dimensional":[73],"planes":[74],"high-dimensional":[76],"space":[77,103],"due":[78],"occlusion":[80],"invisibility,":[82],"respectively.":[83],"Furthermore,":[84],"existing":[85],"assets":[88,166],"small-scale,":[90],"insufficient":[91],"for":[92,106],"large-scale":[93,170,180],"training.":[94,171],"To":[95],"address":[96],"these":[97],"challenges,":[98],"we":[99,126,145],"propose":[100],"latent":[102],"paradigm":[105],"digitization,":[109],"which":[110,138,160],"involves":[111],"compressing":[112],"images":[114],"Gaussians":[116,186],"via":[117],"UV-structured":[119],"VAE,":[120],"along":[121],"DiT-based":[123],"conditional":[124],"generation,":[125],"transform":[127],"ill-posed":[129],"low-to-high-dimensional":[130],"problem":[132],"learnable":[135],"distribution":[136],"shift,":[137],"also":[139],"supports":[140],"end-to-end":[141],"inference.":[142],"In":[143],"addition,":[144],"employ":[146],"optimization":[149],"approach":[150],"combined":[151],"synthetic":[153],"data":[154],"construct":[156],"HGS-1M":[158],"dataset,":[159],"contains":[161],"$1$":[162],"million":[163],"Gaussian":[165],"support":[168],"Experimental":[172],"results":[173],"demonstrate":[174],"that":[175],"our":[176],"paradigm,":[177],"powered":[178],"training,":[181],"produces":[182],"intricate":[188],"textures,":[189],"facial":[190],"details,":[191],"loose":[193],"clothing":[194],"deformation.":[195]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}