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Graph."],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:p>\n                    Generating high-quality triangle meshes is essential for film, gaming, and interactive 3D applications. Mainstream methods rely on mesh serialization and autoregressive processes, which stuggles in effective inference and is sensitive to error accumulation. In this paper, we present\n                    <jats:bold>Nexus<\/jats:bold>\n                    , a diffusion method that achieves holistic mesh generation via decoupled vertex and topology generation. First, we view mesh vertices as sparse voxels organized as an octree and adopt a diffusion model to generate the vertices in a coarse-to-fine manner. Second, for topology modeling, we propose\n                    <jats:italic toggle=\"yes\">Space-time Interval<\/jats:italic>\n                    , as an extension of\n                    <jats:italic toggle=\"yes\">Spacetime Distance<\/jats:italic>\n                    to encode arbitrary edge and face topology into continuous per-vertex embeddings. It allows for a global and efficient recovery of complex topology. We then employ a diffusion model to generate the continuous embeddings on the generated vertices. Extensive experiments on the Objaverse and Toys4K datasets and in-the-wild images demonstrate that our method outperforms state-of-the-art autoregressive and two-stage baselines, effectively circumventing the inherent limitations of sequential mesh modeling. A blind user study from 3D practitioners confirms strong perceptual preference for our results.\n                  <\/jats:p>","DOI":"10.1145\/3811344","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:05:51Z","timestamp":1783062351000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Nexus: Native Mesh Generation with Diffusion"],"prefix":"10.1145","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8926-6092","authenticated-orcid":false,"given":"Hanxiao","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8293-3223","authenticated-orcid":false,"given":"Ying-Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6164-8343","authenticated-orcid":false,"given":"Yuan-Chen","family":"Guo","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7545-3308","authenticated-orcid":false,"given":"Qi-Yuan","family":"Feng","sequence":"additional","affiliation":[{"name":"CS Dept, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2945-552X","authenticated-orcid":false,"given":"Zi-Xin","family":"Zou","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9774-4687","authenticated-orcid":false,"given":"Ding","family":"Liang","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5685-6092","authenticated-orcid":false,"given":"Biao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-4374","authenticated-orcid":false,"given":"Yan-Pei","family":"Cao","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Antonio Alliegro Yawar Siddiqui Tatiana Tommasi and Matthias Nie\u00dfner. 2023. 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