EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation
Keywords: 3D Generation, Auto-regressive Mesh Generation
TL;DR: An auto-regressive auto-encoder that compresses variable-length meshes into fixed-length latent codes.
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.
Primary Area: generative models
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Submission Number: 1123
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