Summary of Meshanything: Artist-created Mesh Generation with Autoregressive Transformers, by Yiwen Chen et al.
MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers
by Yiwen Chen, Tong He, Di Huang, Weicai Ye, Sijin Chen, Jiaxiang Tang, Xin Chen, Zhongang Cai, Lei Yang, Gang Yu, Guosheng Lin, Chi Zhang
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MeshAnything, a model that tackles the challenge of converting 3D assets into Artist-Created Meshes (AMs) that match the quality of meshes created by human artists. Current mesh extraction methods rely on dense faces and ignore geometric features, leading to inefficiencies and lower representation quality. To address this issue, MeshAnything treats mesh extraction as a generation problem, producing AMs aligned with specified shapes. The model comprises a VQ-VAE and a shape-conditioned decoder-only transformer, which learns a mesh vocabulary using the VQ-VAE and then trains on this vocabulary for shape-conditioned autoregressive mesh generation. Experimental results show that MeshAnything generates AMs with hundreds of times fewer faces, improving storage, rendering, and simulation efficiencies while achieving precision comparable to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MeshAnything is a new way to make 3D assets look good in different industries. Right now, it’s hard to convert these assets into the right kind of shape for use. MeshAnything makes it easier by taking any 3D representation and turning it into a special kind of mesh that artists create. This can help with things like storing and using 3D models. The new way works by first learning what different shapes look like, then using that information to create the right mesh. |
Keywords
» Artificial intelligence » Autoregressive » Decoder » Precision » Transformer
