用新编码方法生成高质量3D网格，最多4000面，细节丰富。
EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
- 自回归自编码架构，将网格转为一维序列高效训练
- 支持4000面、512³分辨率的高质量网格生成
- 适合需要高精度3D建模的设计师与科研人员
当前自回归网格生成方法存在不完整、细节不足和泛化能力差的问题。本文提出一种自回归自编码器（ArAE）模型，可在512³空间分辨率下生成最高达4,000个面的高质量3D网格。我们设计了一种新型网格标记化算法，能将三角网格高效压缩为一维标记序列，显著提升训练效率。此外，模型将可变长度网格压缩至固定长度潜在空间，支持训练潜在扩散模型以增强泛化能力。大量实验表明，该模型在点云与图像条件下的网格生成任务中均表现出卓越的质量、多样性和泛化性能。
原文摘要 · Abstract (English)
Current auto-regressive mesh generation methods suffer from issues such as incompleteness, insufficient detail, and poor generalization. In this paper, we propose an Auto-regressive Auto-encoder (ArAE) model capable of generating high-quality 3D meshes with up to 4,000 faces at a spatial resolution of $512^3$. We introduce a novel mesh tokenization algorithm that efficiently compresses triangular meshes into 1D token sequences, significantly enhancing training efficiency. Furthermore, our model compresses variable-length triangular meshes into a fixed-length latent space, enabling training latent diffusion models for better generalization. Extensive experiments demonstrate the superior quality, diversity, and generalization capabilities of our model in both point cloud and image-conditioned mesh generation tasks.
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