Yiwen Chen, Yikai Wang, Yihao Luo, Zhengyi Wang, Zilong Chen, Jun Zhu, Chi Zhang, Guosheng Lin
{"title":"MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization","authors":"Yiwen Chen, Yikai Wang, Yihao Luo, Zhengyi Wang, Zilong Chen, Jun Zhu, Chi Zhang, Guosheng Lin","doi":"arxiv-2408.02555","DOIUrl":null,"url":null,"abstract":"We introduce MeshAnything V2, an autoregressive transformer that generates\nArtist-Created Meshes (AM) aligned to given shapes. It can be integrated with\nvarious 3D asset production pipelines to achieve high-quality, highly\ncontrollable AM generation. MeshAnything V2 surpasses previous methods in both\nefficiency and performance using models of the same size. These improvements\nare due to our newly proposed mesh tokenization method: Adjacent Mesh\nTokenization (AMT). Different from previous methods that represent each face\nwith three vertices, AMT uses a single vertex whenever possible. Compared to\nprevious methods, AMT requires about half the token sequence length to\nrepresent the same mesh in average. Furthermore, the token sequences from AMT\nare more compact and well-structured, fundamentally benefiting AM generation.\nOur extensive experiments show that AMT significantly improves the efficiency\nand performance of AM generation. Project Page:\nhttps://buaacyw.github.io/meshanything-v2/","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.02555","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We introduce MeshAnything V2, an autoregressive transformer that generates
Artist-Created Meshes (AM) aligned to given shapes. It can be integrated with
various 3D asset production pipelines to achieve high-quality, highly
controllable AM generation. MeshAnything V2 surpasses previous methods in both
efficiency and performance using models of the same size. These improvements
are due to our newly proposed mesh tokenization method: Adjacent Mesh
Tokenization (AMT). Different from previous methods that represent each face
with three vertices, AMT uses a single vertex whenever possible. Compared to
previous methods, AMT requires about half the token sequence length to
represent the same mesh in average. Furthermore, the token sequences from AMT
are more compact and well-structured, fundamentally benefiting AM generation.
Our extensive experiments show that AMT significantly improves the efficiency
and performance of AM generation. Project Page:
https://buaacyw.github.io/meshanything-v2/