Summary of Llama-mesh: Unifying 3d Mesh Generation with Language Models, by Zhengyi Wang et al.
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
by Zhengyi Wang, Jonathan Lorraine, Yikai Wang, Hang Su, Jun Zhu, Sanja Fidler, Xiaohui Zeng
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed work leverages large language models (LLMs) to generate 3D meshes within a unified model, enabling conversational 3D generation and mesh understanding. The primary challenge is tokenizing 3D mesh data into discrete tokens for seamless processing by LLMs. To address this, the authors introduce LLaMA-Mesh, a novel approach representing vertex coordinates and face definitions as plain text, allowing direct integration with LLMs without expanding the vocabulary. A supervised fine-tuning (SFT) dataset is constructed to enable pretrained LLMs to generate 3D meshes from text prompts, produce interleaved text and 3D mesh outputs, and understand and interpret 3D meshes. The work demonstrates that LLMs can be fine-tuned to acquire complex spatial knowledge for 3D mesh generation in a text-based format, effectively unifying the 3D and text modalities. LLaMA-Mesh achieves mesh generation quality on par with models trained from scratch while maintaining strong text generation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it possible for computers to generate 3D shapes using only text instructions. It’s like asking a computer to draw a picture of a cat, but instead of drawing it, the computer generates a 3D model of a cat. The researchers used special language models that are already good at understanding and generating human language, and they taught these models how to understand and generate 3D shapes too. This is useful because it means we can have computers create 3D models of things without needing to teach them everything from scratch. It’s like having a magic computer that can draw pictures in 3D! |
Keywords
» Artificial intelligence » Fine tuning » Llama » Supervised » Text generation
