Meshes are the de facto 3D representation in the industry but are labor-intensive to produce. Recently, a line of research has focused on autoregressively generating meshes. This approach processes meshes into a sequence composed of vertices and then generates them vertex by vertex, similar to how a language model generates text. These methods have achieved some success but still struggle to generate complex meshes. One primary reason for this limitation is their inefficient tokenization methods. To address this issue, we introduce MeshAnything V2, an advanced mesh generation model designed to create Artist-Created Meshes that align precisely with specified shapes. A key innovation behind MeshAnything V2 is our novel Adjacent Mesh Tokenization (AMT) method. Unlike traditional approaches that represent each face using three vertices, AMT optimizes this by employing a single vertex wherever feasible, effectively reducing the token sequence length by about half on average. This not only streamlines the tokenization process but also results in more compact and well-structured sequences, enhancing the efficiency of mesh generation. With these improvements, MeshAnything V2 effectively doubles the face limit compared to previous models, delivering superior performance without increasing computational costs. We will make our code and models publicly available. Project Page: https://buaacyw.github.io/meshanything-v2/

翻译：网格是工业界事实上的三维表示方式，但其制作过程劳动密集。最近，一系列研究专注于自回归生成网格。该方法将网格处理为由顶点组成的序列，然后逐个顶点生成，类似于语言模型生成文本。这些方法已取得一定成功，但在生成复杂网格方面仍面临困难。造成这一局限的主要原因之一是其低效的标记化方法。为解决此问题，我们引入了MeshAnything V2，这是一种先进的网格生成模型，旨在创建与指定形状精确对齐的艺术家级网格。MeshAnything V2背后的关键创新是我们新颖的相邻网格标记化方法。与传统方法使用三个顶点表示每个面不同，AMT通过尽可能使用单个顶点来优化表示，从而平均将标记序列长度减少约一半。这不仅简化了标记化过程，还产生了更紧凑且结构良好的序列，提高了网格生成的效率。凭借这些改进，MeshAnything V2与先前模型相比，有效将面数限制提高了一倍，在不增加计算成本的情况下提供了更优的性能。我们将公开代码和模型。项目页面：https://buaacyw.github.io/meshanything-v2/