{"id":"https://openalex.org/W4415536818","doi":"https://doi.org/10.1145/3746027.3755516","title":"TextSplat: Text-Guided Semantic Fusion for Generalizable Gaussian Splatting","display_name":"TextSplat: Text-Guided Semantic Fusion for Generalizable Gaussian Splatting","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415536818","doi":"https://doi.org/10.1145/3746027.3755516"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755516","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101541477","display_name":"Zhicong Wu","orcid":"https://orcid.org/0009-0000-8250-0030"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicong Wu","raw_affiliation_strings":["Institute of Artificial Intelligence, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0009-0000-8250-0030","affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016248876","display_name":"Hongbin Xu","orcid":"https://orcid.org/0000-0002-3455-1527"},"institutions":[{"id":"https://openalex.org/I4210128111","display_name":"Clover Seed (China)","ror":"https://ror.org/037jwt041","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128111"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbin Xu","raw_affiliation_strings":["Bytedance Seed, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3455-1527","affiliations":[{"raw_affiliation_string":"Bytedance Seed, Beijing, China","institution_ids":["https://openalex.org/I4210128111"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101508644","display_name":"Gang Xu","orcid":"https://orcid.org/0000-0002-9159-4662"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Xu","raw_affiliation_strings":["Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9159-4662","affiliations":[{"raw_affiliation_string":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070518797","display_name":"Ping Nie","orcid":"https://orcid.org/0000-0002-4260-879X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Nie","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4260-879X","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110797460","display_name":"Zhixin Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixin Yan","raw_affiliation_strings":["School of Future Technology, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0002-4402-2677","affiliations":[{"raw_affiliation_string":"School of Future Technology, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071555197","display_name":"Jinkai Zheng","orcid":"https://orcid.org/0000-0002-9176-7703"},"institutions":[{"id":"https://openalex.org/I4210113281","display_name":"Lishui Central Hospital","ror":"https://ror.org/023e72x78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210113281"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinkai Zheng","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China and Central Laboratory of Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9176-7703","affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China and Central Laboratory of Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, Hangzhou, China","institution_ids":["https://openalex.org/I4210113281"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083630052","display_name":"Liangqiong Qu","orcid":"https://orcid.org/0000-0001-8235-7852"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Liangqiong Qu","raw_affiliation_strings":["University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-8235-7852","affiliations":[{"raw_affiliation_string":"University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100351468","display_name":"Ming Li","orcid":"https://orcid.org/0000-0002-7852-0159"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Li","raw_affiliation_strings":["Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-7852-0159","affiliations":[{"raw_affiliation_string":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038612499","display_name":"Liqiang Nie","orcid":"https://orcid.org/0000-0003-1476-0273"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liqiang Nie","raw_affiliation_strings":["Harbin Institute of Technology (Shenzhen), Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-1476-0273","affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology (Shenzhen), Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8478","last_page":"8487"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9944999814033508,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9944999814033508,"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":0.9810000061988831,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.98089998960495,"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/focus","display_name":"Focus (optics)","score":0.6638000011444092},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5508999824523926},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5339000225067139},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5311999917030334},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4645000100135803},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4156999886035919},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.3903000056743622},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3894999921321869},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3765000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8687999844551086},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6638000011444092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6080999970436096},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5508999824523926},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5339000225067139},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5311999917030334},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3894999921321869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3497999906539917},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3431999981403351},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3353999853134155},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3001999855041504},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.28040000796318054},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27320000529289246},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755516","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4100402671","display_name":null,"funder_award_id":"62306253","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":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2471962767","https://openalex.org/W2962785568","https://openalex.org/W3111114371","https://openalex.org/W3138516171","https://openalex.org/W3176368002","https://openalex.org/W3204859697","https://openalex.org/W3206736890","https://openalex.org/W3215023725","https://openalex.org/W3215769467","https://openalex.org/W3216476011","https://openalex.org/W4200150166","https://openalex.org/W4206760982","https://openalex.org/W4312815172","https://openalex.org/W4312933868","https://openalex.org/W4312955971","https://openalex.org/W4385259380","https://openalex.org/W4385318467","https://openalex.org/W4386075846","https://openalex.org/W4386076472","https://openalex.org/W4390873429","https://openalex.org/W4391092744","https://openalex.org/W4391407087","https://openalex.org/W4402667896","https://openalex.org/W4402715971","https://openalex.org/W4402727668","https://openalex.org/W4402733565","https://openalex.org/W4402753396"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,53,108,127],"Generalizable":[3,65],"Gaussian":[4,18,66,130],"Splatting":[5,19,67],"have":[6],"enabled":[7],"robust":[8],"3D":[9,129],"reconstruction":[10],"from":[11],"sparse":[12],"input":[13],"views":[14],"by":[15],"utilizing":[16],"feed-forward":[17],"models,":[20],"achieving":[21],"superior":[22],"cross-scene":[23],"generalization.":[24],"However,":[25],"while":[26],"many":[27],"methods":[28,149],"focus":[29],"on":[30,139],"geometric":[31],"consistency,":[32],"they":[33],"often":[34],"neglect":[35],"the":[36,80,89,99,109,119,155],"potential":[37],"of":[38,157],"text-driven":[39,64],"guidance":[40],"to":[41,76,147],"enhance":[42],"semantic":[43,96,135],"understanding,":[44],"which":[45],"is":[46],"crucial":[47],"for":[48,85,94,103],"accurately":[49],"reconstructing":[50],"fine-grained":[51],"details":[52],"complex":[54],"scenes.":[55],"To":[56],"address":[57],"this":[58],"limitation,":[59],"we":[60],"propose":[61],"TextSplat-the":[62],"first":[63],"framework.":[68,159],"Specifically,":[69],"our":[70,158],"framework":[71],"employs":[72],"three":[73],"parallel":[74],"modules":[75],"obtain":[77],"complementary":[78],"representations:":[79],"Diffusion":[81],"Prior":[82],"Depth":[83],"Estimator":[84],"accurate":[86],"depth":[87],"information,":[88,97],"Semantic":[90,111],"Aware":[91],"Segmentation":[92],"Network":[93,102],"detailed":[95,134],"and":[98,121],"Multi-View":[100],"Interaction":[101],"refined":[104],"cross-view":[105],"features.":[106],"Then,":[107],"Text-Guided":[110],"Fusion":[112],"Module,":[113],"these":[114],"representations":[115],"are":[116],"integrated":[117],"via":[118],"text-guided":[120],"attention-based":[122],"feature":[123],"aggregation":[124],"mechanism,":[125],"resulting":[126],"enhanced":[128],"parameters":[131],"enriched":[132],"with":[133],"cues.":[136],"Experimental":[137],"results":[138],"various":[140],"benchmark":[141],"datasets":[142],"demonstrate":[143],"improved":[144],"performance":[145],"compared":[146],"existing":[148],"across":[150],"multiple":[151],"evaluation":[152],"metrics,":[153],"validating":[154],"effectiveness":[156],"The":[160],"code":[161],"will":[162],"be":[163],"publicly":[164],"available.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-25T00:00:00"}
