1. EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
- Author
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Tang, Jiaxiang, Li, Zhaoshuo, Hao, Zekun, Liu, Xian, Zeng, Gang, Liu, Ming-Yu, and Zhang, Qinsheng
- Subjects
Computer Science - Computer Vision and Pattern Recognition - 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., Comment: Project Page: https://research.nvidia.com/labs/dir/edgerunner/
- Published
- 2024