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arXiv · 2409.18114

EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation

Jiaxiang Tang·Zhaoshuo Li·Zekun Hao·Xian Liu·Gang Zeng·Ming-Yu Liu·Qinsheng Zhang
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Abstract

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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Jiaxiang Tang, Zhaoshuo Li, Zekun Hao, Xian Liu, Gang Zeng, Ming-Yu Liu, Qinsheng Zhang. 2024-09-26. EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation. https://arxiv.org/abs/2409.18114

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