MeshAnything V2：使用相邻网格生成由艺术家创建的网格的标记化

MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization

August 5, 2024

作者: Yiwen Chen, Yikai Wang, Yihao Luo, Zhengyi Wang, Zilong Chen, Jun Zhu, Chi Zhang, Guosheng Lin

cs.AI

摘要

我们介绍 MeshAnything V2，这是一种自回归变换器，用于生成与给定形状对齐的艺术家创建的网格（AM）。它可以与各种3D资产生产流程集成，实现高质量、高度可控的AM生成。MeshAnything V2在相同规模的模型下，效率和性能均超越先前的方法。这些改进归功于我们新提出的网格标记方法：相邻网格标记（AMT）。与以往将每个面用三个顶点表示的方法不同，AMT在可能的情况下使用单个顶点。与以往的方法相比，AMT平均需要大约一半的标记序列长度来表示相同的网格。此外，来自AMT的标记序列更紧凑、结构更良好，从根本上有利于AM生成。我们的大量实验证明，AMT显著提高了AM生成的效率和性能。项目页面：https://buaacyw.github.io/meshanything-v2/

English

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/