LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
Authors: name: Zhengyi Wang, name: Jonathan Lorraine, name: Yikai Wang, name: Hang Su, name: Jun Zhu, name: Sanja Fidler, name: Xiaohui Zeng
Organization: NVIDIA

This work explores expanding the capabilities of large language models (LLMs)
pretrained on text to generate 3D meshes within a unified model. This offers
key advantages of (1) leveraging spatial knowledge already embedded in LLMs,
derived from textual sources like 3D tutorials, and (2) enabling conversational
3D generation and mesh understanding. A primary challenge is effectively
tokenizing 3D mesh data into discrete tokens that LLMs can process seamlessly.
To address this, we introduce LLaMA-Mesh, a novel approach that represents the
vertex coordinates and face definitions of 3D meshes as plain text, allowing
direct integration with LLMs without expanding the vocabulary. We construct a
supervised fine-tuning (SFT) dataset enabling pretrained LLMs to (1) generate
3D meshes from text prompts, (2) produce interleaved text and 3D mesh outputs
as required, and (3) understand and interpret 3D meshes. Our work is the first
to demonstrate 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.