Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substantial progress, such token sequences inevitably reuse vertices multiple times to fully represent manifold meshes, as each vertex is shared by multiple faces. This redundancy leads to excessively long token sequences and inefficient generation processes. In this paper, we propose an efficient framework that generates artistic meshes by treating vertices and faces separately, significantly reducing redundancy. We employ an autoregressive model solely for vertex generation, decreasing the token count to approximately 23% of that required by the most compact existing tokenizer. Next, we leverage a bidirectional transformer to complete the mesh in a single step by capturing inter-vertex relationships and constructing the adjacency matrix that defines the mesh faces. To further improve the generation quality, we introduce a fidelity enhancer to refine vertex positioning into more natural arrangements and propose a post-processing framework to remove undesirable edge connections. Experimental results show that our method achieves more than 8x faster speed on mesh generation compared to state-of-the-art approaches, while producing higher mesh quality.

翻译：近年来，网格生成方法通常将三角形网格标记化为令牌序列，并训练自回归模型以顺序生成这些令牌。尽管取得了显著进展，但此类令牌序列不可避免地会多次复用顶点以完整表示流形网格，因为每个顶点被多个面共享。这种冗余导致令牌序列过长且生成过程效率低下。本文提出一种高效框架，通过分别处理顶点和面来生成艺术网格，显著减少了冗余。我们仅采用自回归模型进行顶点生成，将所需令牌数量降至现有最紧凑标记器的约23%。随后，我们利用双向Transformer通过捕获顶点间关系并构建定义网格面的邻接矩阵，在单步内完成网格生成。为进一步提升生成质量，我们引入保真度增强器以优化顶点位置至更自然的排布，并提出后处理框架以消除不良边连接。实验结果表明，相较于最先进方法，我们的方法在网格生成速度上提升超过8倍，同时生成更高质量的网格。