{"id":"https://openalex.org/W4389539690","doi":"https://doi.org/10.1145/3610548.3618194","title":"MatFusion: A Generative Diffusion Model for SVBRDF Capture","display_name":"MatFusion: A Generative Diffusion Model for SVBRDF Capture","publication_year":2023,"publication_date":"2023-12-10","ids":{"openalex":"https://openalex.org/W4389539690","doi":"https://doi.org/10.1145/3610548.3618194"},"language":"en","primary_location":{"id":"doi:10.1145/3610548.3618194","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3610548.3618194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIGGRAPH Asia 2023 Conference Papers","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2406.06539","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092489700","display_name":"Sam Sartor","orcid":"https://orcid.org/0009-0001-1915-6887"},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sam Sartor","raw_affiliation_strings":["College of William & Mary, United States of America"],"affiliations":[{"raw_affiliation_string":"College of William & Mary, United States of America","institution_ids":["https://openalex.org/I16285277"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000811147","display_name":"Pieter Peers","orcid":"https://orcid.org/0000-0001-7621-9808"},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pieter Peers","raw_affiliation_strings":["College of William & Mary, United States of America"],"affiliations":[{"raw_affiliation_string":"College of William & Mary, United States of America","institution_ids":["https://openalex.org/I16285277"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5092489700"],"corresponding_institution_ids":["https://openalex.org/I16285277"],"apc_list":null,"apc_paid":null,"fwci":3.8483,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.95095642,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9891999959945679,"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":0.9891999959945679,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9745000004768372,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9700999855995178,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/generative-model","display_name":"Generative model","score":0.6075690388679504},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6027258038520813},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5773394703865051},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5414230227470398},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3331238925457001},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09008839726448059},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.06810209155082703}],"concepts":[{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6075690388679504},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6027258038520813},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5773394703865051},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5414230227470398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3331238925457001},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09008839726448059},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.06810209155082703}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3610548.3618194","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3610548.3618194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIGGRAPH Asia 2023 Conference Papers","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2406.06539","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.06539","pdf_url":"https://arxiv.org/pdf/2406.06539","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:scholarworks.wm.edu:etd-7493","is_oa":true,"landing_page_url":"https://scholarworks.wm.edu/etd/1697552550","pdf_url":null,"source":{"id":"https://openalex.org/S4306401254","display_name":"W&M Publish (College of William & Mary)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I16285277","host_organization_name":"William & Mary","host_organization_lineage":["https://openalex.org/I16285277"],"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":"Dissertations, Theses, and Masters Projects","raw_type":"text"},{"id":"doi:10.21220/s2-ar28-g979","is_oa":true,"landing_page_url":"https://doi.org/10.21220/s2-ar28-g979","pdf_url":null,"source":{"id":"https://openalex.org/S7407053363","display_name":"William & Mary School of Arts & Sciences","issn_l":null,"issn":[],"is_oa":false,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2406.06539","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2406.06539","pdf_url":"https://arxiv.org/pdf/2406.06539","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6945029026","display_name":null,"funder_award_id":"IIS-1909028","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W2475362300","https://openalex.org/W2736596523","https://openalex.org/W2754836122","https://openalex.org/W2811490555","https://openalex.org/W2898348749","https://openalex.org/W2958302123","https://openalex.org/W2962785568","https://openalex.org/W2963650738","https://openalex.org/W3035384201","https://openalex.org/W3036167779","https://openalex.org/W3090135481","https://openalex.org/W3101452841","https://openalex.org/W3105013723","https://openalex.org/W3106975417","https://openalex.org/W3109203742","https://openalex.org/W3155072588","https://openalex.org/W3175528029","https://openalex.org/W3183743915","https://openalex.org/W3186445620","https://openalex.org/W3196078005","https://openalex.org/W4205486372","https://openalex.org/W4205633319","https://openalex.org/W4224278419","https://openalex.org/W4281479141","https://openalex.org/W4283777353","https://openalex.org/W4283825676","https://openalex.org/W4311802430","https://openalex.org/W4312933868","https://openalex.org/W4385537492","https://openalex.org/W4390873054","https://openalex.org/W6779823529"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W3174044702","https://openalex.org/W2967848559","https://openalex.org/W4299831724"],"abstract_inverted_index":{"We":[0,137],"formulate":[1],"SVBRDF":[2,24,41,107,116,147,177],"estimation":[3,178],"from":[4,64,118,123],"photographs":[5],"as":[6,50],"a":[7,21,29,51,55,65,80,161],"diffusion":[8,25,42,57,108,148],"task.":[9],"To":[10],"model":[11,27,58,75],"the":[12,61,83,98,105,119,125,129,134,139],"distribution":[13],"of":[14,32,141,154],"spatially":[15,36],"varying":[16,37],"materials,":[17],"we":[18,112],"first":[19],"train":[20],"novel":[22],"unconditional":[23],"backbone":[26,43,73],"on":[28,82,151],"large":[30],"set":[31],"312,":[33],"165":[34],"synthetic":[35],"material":[38,62],"exemplars.":[39],"This":[40],"model,":[44],"named":[45],"MatFusion,":[46],"can":[47,89,113,127],"then":[48],"serve":[49],"basis":[52],"for":[53,100,160],"refining":[54,145],"conditional":[56,106],"to":[59],"estimate":[60],"properties":[63],"photograph":[66,122,163],"under":[67,164],"controlled":[68],"or":[69,172],"uncontrolled":[70],"lighting.":[71],"Our":[72],"MatFusion":[74],"is":[76],"trained":[77],"using":[78],"only":[79],"loss":[81],"reflectance":[84],"properties,":[85],"and":[86,157],"therefore":[87],"refinement":[88],"be":[90],"paired":[91],"with":[92],"more":[93],"expensive":[94],"rendering":[95],"methods":[96],"without":[97],"need":[99],"backpropagation":[101],"during":[102],"training.":[103],"Because":[104],"models":[109,149],"are":[110],"generative,":[111],"synthesize":[114],"multiple":[115],"estimates":[117],"same":[120],"input":[121],"which":[124],"user":[126],"select":[128],"one":[130],"that":[131,159],"best":[132],"matches":[133],"users\u2019":[135],"expectation.":[136],"demonstrate":[138],"flexibility":[140],"our":[142,168],"method":[143,169],"by":[144],"different":[146,152],"conditioned":[150],"types":[153],"incident":[155],"lighting,":[156],"show":[158],"single":[162],"colocated":[165],"flash":[166],"lighting":[167],"achieves":[170],"equal":[171],"better":[173],"accuracy":[174],"than":[175],"existing":[176],"methods.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":14}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}