We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milestone unlocks the potential to utilize mesh data with significantly more faces, thereby enhancing detail richness and improving generation robustness. Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images. Our model demonstrates the capability to generate meshes with intricate details and accurate topology, achieving SoTA performance on mesh generation and reaching the level for direct product usage.

翻译：我们提出了一种压缩且高效的网格表示方法——分块与分片标记化（BPT），该方法能够促进生成超过8000个面的网格。BPT通过采用分块索引与分片聚合技术压缩网格序列，使其长度相较于原始序列减少约75%。这一压缩里程碑释放了利用具有显著更多面数的网格数据的潜力，从而增强了细节丰富度并提升了生成鲁棒性。借助BPT，我们构建了一个基于规模化网格数据训练的基础网格生成模型，以支持对点云和图像的灵活控制。我们的模型展现了生成具有复杂细节和精确拓扑结构网格的能力，在网格生成任务上实现了最先进的性能，并达到了可直接用于产品生产的水平。