{"id":"https://openalex.org/W7143421602","doi":"https://doi.org/10.48550/arxiv.2603.26661","title":"GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation","display_name":"GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7143421602","doi":"https://doi.org/10.48550/arxiv.2603.26661"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26661","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26661","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.26661","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130987378","display_name":"Nicolas von L\u00fctzow","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"von L\u00fctzow, Nicolas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107528038","display_name":"Barbara R\u00f6ssle","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R\u00f6ssle, Barbara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063300935","display_name":"Katharina Schmid","orcid":"https://orcid.org/0000-0001-6018-9245"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schmid, Katharina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130971355","display_name":"Matthias Nie\u00dfner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nie\u00dfner, Matthias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5662000179290771,"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":0.5662000179290771,"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":0.22949999570846558,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.057100001722574234,"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/autoregressive-model","display_name":"Autoregressive model","score":0.8125},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6430000066757202},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5598999857902527},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.503600001335144},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4941999912261963},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.43709999322891235},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.424699991941452},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4050000011920929}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7006999850273132},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6430000066757202},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5598999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.550599992275238},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.503600001335144},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.43709999322891235},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43309998512268066},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.424699991941452},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3653999865055084},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.35910001397132874},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.3343999981880188},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.31060001254081726},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26660001277923584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26661","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26661","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.26661","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26661","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":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Most":[0],"recent":[1],"advances":[2],"in":[3],"3D":[4,29,37,53,71,141],"generative":[5],"modeling":[6,118],"rely":[7],"on":[8,121],"diffusion":[9],"or":[10],"flow-matching":[11],"formulations.":[12],"We":[13,40],"instead":[14],"explore":[15],"a":[16,23,46,51,67,134],"fully":[17],"autoregressive":[18,117,131],"alternative":[19],"and":[20,64,81,103,114,139],"introduce":[21],"GaussianGPT,":[22],"transformer-based":[24],"model":[25],"that":[26,86],"directly":[27],"generates":[28],"Gaussians":[30],"via":[31,101],"next-token":[32],"prediction,":[33],"thus":[34],"facilitating":[35],"full":[36],"scene":[38],"generation.":[39,142],"first":[41],"compress":[42],"Gaussian":[43],"primitives":[44],"into":[45],"discrete":[47],"latent":[48],"grid":[49],"using":[50,66],"sparse":[52],"convolutional":[54],"autoencoder":[55],"with":[56,70,125],"vector":[57],"quantization.":[58],"The":[59],"resulting":[60],"tokens":[61],"are":[62],"serialized":[63],"modeled":[65],"causal":[68],"transformer":[69],"rotary":[72],"positional":[73],"embedding,":[74],"enabling":[75],"sequential":[76],"generation":[77,105],"of":[78,116],"spatial":[79],"structure":[80],"appearance.":[82],"Unlike":[83],"diffusion-based":[84],"methods":[85],"refine":[87],"scenes":[88,93],"holistically,":[89],"our":[90],"formulation":[91,108],"constructs":[92],"step-by-step,":[94],"naturally":[95],"supporting":[96],"completion,":[97],"outpainting,":[98],"controllable":[99,138],"sampling":[100],"temperature,":[102],"flexible":[104],"horizons.":[106],"This":[107],"leverages":[109],"the":[110],"compositional":[111],"inductive":[112],"biases":[113],"scalability":[115],"while":[119],"operating":[120],"explicit":[122],"representations":[123],"compatible":[124],"modern":[126],"neural":[127],"rendering":[128],"pipelines,":[129],"positioning":[130],"transformers":[132],"as":[133],"complementary":[135],"paradigm":[136],"for":[137],"context-aware":[140]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-31T00:00:00"}