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

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arxiv 2409.18114 v1 pith:74I5OMS2 submitted 2024-09-26 cs.CV

Jiaxiang Tang , Zhaoshuo Li , Zekun Hao , Xian Liu , Gang Zeng , Ming-Yu Liu , Qinsheng Zhang This is my paper · ORCID
classification cs.CV
keywords meshauto-regressivegeneralizationgenerationmeshesmodelauto-encodercompresses
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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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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Nexus replaces autoregressive mesh serialization with two coupled diffusion models — octree vertex generation and a latent topology generator — claiming stronger geometry and perceptual quality on Objaverse and Toys4K.

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  5. Efficient Part-level 3D Object Generation via Dual Volume Packing

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    PartCrafter generates several separable 3D part meshes at once from a single image by fine-tuning a pretrained 3D diffusion transformer with part identity tokens and local-global attention.

  7. XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding

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    XSpecMesh speeds up auto-regressive mesh generation by about 1.7x using multi-head speculative decoding with cross-attention heads and a probability threshold verification, while keeping output quality close to the ba...

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