EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
<|reference_start|>EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation: 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.<|reference_end|>
@article{tang2024edgerunner:,
    title={EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation},
    author={Jiaxiang Tang, Zhaoshuo Li, Zekun Hao, Xian Liu, Gang Zeng, Ming-Yu
  Liu, Qinsheng Zhang},
    journal={arXiv preprint arXiv:2409.18114},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18114},
    primaryClass={cs.CV}
}