The reviewed record of science sign in
Pith

arxiv: 2311.15475 · v1 · pith:L77WN5NA · submitted 2023-11-27 · cs.CV · cs.LG

MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers

Yawar Siddiqui , Antonio Alliegro , Alexey Artemov , Tatiana Tommasi , Daniele Sirigatti , Vladislav Rosov , Angela Dai , Matthias Nie{\ss}ner This is my paper

Reviewed by Pithpith:L77WN5NAopen to challenge →

classification cs.CV cs.LG
keywords meshestriangleembeddingsgeneratingmeshmeshgptapproachautoregressively
0
0 comments X
read the original abstract

We introduce MeshGPT, a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes, in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields. Inspired by recent advances in powerful large language models, we adopt a sequence-based approach to autoregressively generate triangle meshes as sequences of triangles. We first learn a vocabulary of latent quantized embeddings, using graph convolutions, which inform these embeddings of the local mesh geometry and topology. These embeddings are sequenced and decoded into triangles by a decoder, ensuring that they can effectively reconstruct the mesh. A transformer is then trained on this learned vocabulary to predict the index of the next embedding given previous embeddings. Once trained, our model can be autoregressively sampled to generate new triangle meshes, directly generating compact meshes with sharp edges, more closely imitating the efficient triangulation patterns of human-crafted meshes. MeshGPT demonstrates a notable improvement over state of the art mesh generation methods, with a 9% increase in shape coverage and a 30-point enhancement in FID scores across various categories.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

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

  1. Mat\'ern Noise for Triangulation-Agnostic Flow Matching on Meshes

    cs.GR 2026-05 unverdicted novelty 7.0

    Proposes discretized Matérn process noise for triangulation-agnostic flow matching on meshes with PoissonNet denoiser, tested on elastic states and humanoid poses for meshes exceeding one million triangles.

  2. MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer

    cs.CV 2026-06 unverdicted novelty 6.0

    MeshFlow uses a contrastive MeshVAE for compact mesh latents and a flow transformer for parallel generation, claiming 18x speedup over autoregressive methods with high accuracy on standard metrics.

  3. Bounding Global and Local Compression Error of Signal Parameterizations

    eess.IV 2026-05 unverdicted novelty 6.0

    A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.

  4. CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models

    cs.CV 2026-01 unverdicted novelty 5.0

    CG-MLLM is a multimodal LLM using a Mixture-of-Transformer architecture with separate TokenAR and BlockAR components integrated with a pre-trained vision-language backbone and 3D VAE to enable 3D captioning and high-f...