{"id":"https://openalex.org/W7163593799","doi":"https://doi.org/10.48550/arxiv.2606.04688","title":"MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation","display_name":"MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163593799","doi":"https://doi.org/10.48550/arxiv.2606.04688"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.04688","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04688","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.2606.04688","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137873198","display_name":"Jiale Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiale","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137828318","display_name":"Wang Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137817880","display_name":"Ying Shan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Ying","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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9208999872207642,"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":0.9208999872207642,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.025200000032782555,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.015699999406933784,"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":0.6632000207901001},{"id":"https://openalex.org/keywords/polygon-mesh","display_name":"Polygon mesh","score":0.6539000272750854},{"id":"https://openalex.org/keywords/weaving","display_name":"Weaving","score":0.5640000104904175},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.47369998693466187},{"id":"https://openalex.org/keywords/mesh-generation","display_name":"Mesh generation","score":0.4440000057220459},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44269999861717224},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.438400000333786},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.39559999108314514},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.33559998869895935}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6632000207901001},{"id":"https://openalex.org/C31487907","wikidata":"https://www.wikidata.org/wiki/Q1154597","display_name":"Polygon mesh","level":2,"score":0.6539000272750854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6424999833106995},{"id":"https://openalex.org/C54525549","wikidata":"https://www.wikidata.org/wiki/Q2553445","display_name":"Weaving","level":2,"score":0.5640000104904175},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5530999898910522},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.47369998693466187},{"id":"https://openalex.org/C181145010","wikidata":"https://www.wikidata.org/wiki/Q4418033","display_name":"Mesh generation","level":3,"score":0.4440000057220459},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.438400000333786},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.39559999108314514},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32510000467300415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C170589453","wikidata":"https://www.wikidata.org/wiki/Q7940896","display_name":"Volume mesh","level":4,"score":0.30160000920295715},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28600001335144043},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C4069607","wikidata":"https://www.wikidata.org/wiki/Q868732","display_name":"Aliasing","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.04688","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04688","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.2606.04688","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04688","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":{"Autoregressive":[0],"mesh":[1,69],"generation":[2,48,70,128],"has":[3],"gained":[4],"attention":[5],"by":[6,76],"tokenizing":[7],"meshes":[8,168],"into":[9,98],"sequences":[10,34],"and":[11,35,41,120,174],"training":[12],"models":[13],"in":[14,102,140],"a":[15,72,90,123,141,160],"language-modeling":[16],"fashion.":[17],"However,":[18],"existing":[19],"approaches":[20],"suffer":[21],"from":[22],"two":[23],"fundamental":[24],"limitations:":[25],"(i)":[26],"low":[27],"tokenization":[28],"efficiency,":[29],"which":[30],"yields":[31],"long":[32],"token":[33,113],"prevents":[36],"scaling":[37],"to":[38,117,171],"high-poly":[39],"meshes,":[40],"(ii)":[42],"absence":[43],"of":[44,83,164],"geometry-aware":[45],"guidance,":[46],"as":[47,71,109,122],"is":[49,89],"conditioned":[50],"only":[51],"on":[52],"global":[53],"shape":[54],"embeddings":[55],"rather":[56],"than":[57],"local":[58],"surface":[59,73],"cues.":[60],"We":[61],"introduce":[62],"MeshWeaver,":[63],"an":[64],"autoregressive":[65],"framework":[66],"that":[67,94,126,157],"treats":[68],"weaving":[74],"process":[75,101],"directly":[77],"predicting":[78],"the":[79,99,130,148],"next":[80],"vertex":[81,110,138],"instead":[82],"independent":[84],"coordinates.":[85],"At":[86],"its":[87],"core":[88],"multi-level":[91],"sparse-voxel":[92],"encoder":[93],"injects":[95],"geometric":[96,177],"context":[97],"generative":[100,149],"three":[103],"complementary":[104],"ways:":[105],"providing":[106],"voxel":[107,118],"features":[108],"representations,":[111],"guiding":[112],"prediction":[114,139],"via":[115],"cross-attention":[116],"features,":[119],"serving":[121],"structural":[124],"scaffold":[125],"constrains":[127],"around":[129],"input":[131],"surface.":[132],"Our":[133],"hierarchical":[134],"design":[135],"enables":[136],"coarse-to-fine":[137],"single":[142],"decoding":[143],"step,":[144],"while":[145],"tightly":[146],"coupling":[147],"model":[150],"with":[151,169],"3D":[152],"geometry.":[153],"Extensive":[154],"experiments":[155],"demonstrate":[156],"MeshWeaver":[158],"achieves":[159],"state-of-the-art":[161],"compression":[162],"ratio":[163],"18%,":[165],"can":[166],"generate":[167],"up":[170],"16K":[172],"faces,":[173],"significantly":[175],"improves":[176],"fidelity":[178],"over":[179],"prior":[180],"approaches.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-05T00:00:00"}