MeshLLM: Empowering Large Language Models to Progressively Understand
  and Generate 3D Mesh
Authors: name: Shuangkang Fang, name: I-Chao Shen, name: Yufeng Wang, name: Yi-Hsuan Tsai, name: Yi Yang, name: Shuchang Zhou, name: Wenrui Ding, name: Takeo Igarashi, name: Ming-Hsuan Yang

We present MeshLLM, a novel framework that leverages large language models
(LLMs) to understand and generate text-serialized 3D meshes. Our approach
addresses key limitations in existing methods, including the limited dataset
scale when catering to LLMs' token length and the loss of 3D structural
information during mesh serialization. We introduce a Primitive-Mesh
decomposition strategy, which divides 3D meshes into structurally meaningful
subunits. This enables the creation of a large-scale dataset with 1500k+
samples, almost 50 times larger than previous methods, which aligns better with
the LLM scaling law principles. Furthermore, we propose inferring face
connectivity from vertices and local mesh assembly training strategies,
significantly enhancing the LLMs' ability to capture mesh topology and spatial
structures. Experiments show that MeshLLM outperforms the state-of-the-art
LLaMA-Mesh in both mesh generation quality and shape understanding,
highlighting its great potential in processing text-serialized 3D meshes.