EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
Jiaxiang Tang Zhaoshuo Li Zekun Hao Xian Liu Gang Zeng Ming-Yu Liu Qinsheng Zhang
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.
3D Generation, Auto-regressive Mesh Generation
An auto-regressive auto-encoder that compresses variable-length meshes into fixed-length latent codes.