{"id":"https://openalex.org/W4417119493","doi":"https://doi.org/10.1109/3dv69130.2026.00079","title":"Parametric Gaussian Human Model: Generalizable Prior for Efficient and Realistic Human Avatar Modeling","display_name":"Parametric Gaussian Human Model: Generalizable Prior for Efficient and Realistic Human Avatar Modeling","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W4417119493","doi":"https://doi.org/10.1109/3dv69130.2026.00079"},"language":"en","primary_location":{"id":"doi:10.1109/3dv69130.2026.00079","is_oa":false,"landing_page_url":"https://doi.org/10.1109/3dv69130.2026.00079","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on 3D Vision (3DV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.06645","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101556377","display_name":"Cheng Peng","orcid":"https://orcid.org/0000-0002-0618-7887"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Peng","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112992364","display_name":"Jingxiang Sun","orcid":"https://orcid.org/0009-0003-9916-9585"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingxiang Sun","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008293262","display_name":"Yushuo Chen","orcid":"https://orcid.org/0000-0002-9091-6711"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yushuo Chen","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059798354","display_name":"Zhaoqi Su","orcid":"https://orcid.org/0000-0003-3651-8373"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoqi Su","raw_affiliation_strings":["ByteDance","Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ByteDance","institution_ids":[]},{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084552425","display_name":"Zhuo Su","orcid":"https://orcid.org/0000-0002-6090-0110"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuo Su","raw_affiliation_strings":["ByteDance","Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ByteDance","institution_ids":[]},{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032875389","display_name":"Yebin Liu","orcid":"https://orcid.org/0000-0003-3215-0225"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yebin Liu","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01309887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"771","last_page":"782"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.49160000681877136,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.49160000681877136,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.15719999372959137,"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":0.10140000283718109,"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/monocular","display_name":"Monocular","score":0.6732000112533569},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6549000144004822},{"id":"https://openalex.org/keywords/avatar","display_name":"Avatar","score":0.6518999934196472},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5471000075340271},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5020999908447266},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4269999861717224},{"id":"https://openalex.org/keywords/virtual-actor","display_name":"Virtual actor","score":0.36980000138282776},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3407999873161316},{"id":"https://openalex.org/keywords/human-visual-system-model","display_name":"Human visual system model","score":0.33550000190734863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712000012397766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6955999732017517},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6732000112533569},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6549000144004822},{"id":"https://openalex.org/C2777365542","wikidata":"https://www.wikidata.org/wiki/Q83090","display_name":"Avatar","level":2,"score":0.6518999934196472},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6388000249862671},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5471000075340271},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5020999908447266},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C150303390","wikidata":"https://www.wikidata.org/wiki/Q1983852","display_name":"Virtual actor","level":3,"score":0.36980000138282776},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3407999873161316},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C181095308","wikidata":"https://www.wikidata.org/wiki/Q1541599","display_name":"Geometric primitive","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.2806999981403351},{"id":"https://openalex.org/C2781089380","wikidata":"https://www.wikidata.org/wiki/Q5936753","display_name":"Human-body model","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C194969405","wikidata":"https://www.wikidata.org/wiki/Q170519","display_name":"Virtual reality","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/3dv69130.2026.00079","is_oa":false,"landing_page_url":"https://doi.org/10.1109/3dv69130.2026.00079","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on 3D Vision (3DV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2506.06645","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.06645","pdf_url":"https://arxiv.org/pdf/2506.06645","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2506.06645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.06645","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":"pmh:oai:arXiv.org:2506.06645","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.06645","pdf_url":"https://arxiv.org/pdf/2506.06645","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Photorealistic":[0],"and":[1,13,29,41,61,72,95,102,116,131],"animatable":[2],"human":[3,66],"avatars":[4,166],"are":[5],"a":[6,59,85,98,104],"key":[7],"enabler":[8],"for":[9,70,176],"virtual/augmented":[10],"reality,":[11],"telepresence,":[12],"digital":[14],"entertainment.":[15],"While":[16],"recent":[17],"advances":[18],"in":[19],"3D":[20],"Gaussian":[21,55,110],"Splatting":[22],"(3DGS)":[23],"have":[24],"greatly":[25],"improved":[26],"rendering":[27,126],"quality":[28,127],"efficiency,":[30],"existing":[31],"methods":[32],"still":[33],"face":[34],"fundamental":[35],"challenges,":[36],"including":[37],"time-consuming":[38],"per-subject":[39],"optimization":[40,144],"poor":[42],"generalization":[43],"under":[44,128],"sparse":[45],"monocular":[46,77,178],"inputs.":[47],"In":[48],"this":[49],"work,":[50],"we":[51],"present":[52],"the":[53],"Parametric":[54],"Human":[56],"Model":[57],"(PGHM),":[58],"generalizable":[60],"efficient":[62,135,153],"framework":[63],"that":[64,90,108,148],"integrates":[65],"priors":[67],"into":[68,97],"3DGS":[69],"fast":[71],"high-fidelity":[73],"avatar":[74,179],"reconstruction":[75],"from":[76],"videos.":[78],"PGHM":[79,149],"introduces":[80],"two":[81],"core":[82],"components:":[83],"(1)":[84],"UV-aligned":[86],"latent":[87],"identity":[88],"map":[89],"compactly":[91],"encodes":[92],"subject-specific":[93],"geometry":[94],"appearance":[96],"learnable":[99],"feature":[100],"tensor;":[101],"(2)":[103],"disentangled":[105],"Multi-Head":[106],"U-Net":[107],"predicts":[109],"attributes":[111],"by":[112],"decomposing":[113],"static,":[114],"pose-dependent,":[115],"view-dependent":[117],"components":[118],"via":[119],"conditioned":[120],"decoders.":[121],"This":[122],"design":[123],"enables":[124],"robust":[125],"challenging":[129],"poses":[130],"viewpoints,":[132],"while":[133],"allowing":[134],"subject":[136,163],"adaptation":[137],"without":[138],"requiring":[139,157],"multiview":[140],"capture":[141],"or":[142],"long":[143],"time.":[145],"Experiments":[146],"show":[147],"is":[150],"significantly":[151],"more":[152],"than":[154],"optimization-from-scratch":[155],"methods,":[156],"only":[158],"approximately":[159],"20":[160],"minutes":[161],"per":[162],"to":[164],"produce":[165],"with":[167],"comparable":[168],"visual":[169],"quality,":[170],"thereby":[171],"demonstrating":[172],"its":[173],"practical":[174],"applicability":[175],"real-world":[177],"creation.":[180]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}