ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and
  Understanding
Authors: name: Junliang Ye, name: Zhengyi Wang, name: Ruowen Zhao, name: Shenghao Xie, name: Jun Zhu

Recently, the powerful text-to-image capabilities of ChatGPT-4o have led to
growing appreciation for native multimodal large language models. However, its
multimodal capabilities remain confined to images and text. Yet beyond images,
the ability to understand and generate 3D content is equally crucial. To
address this gap, we propose ShapeLLM-Omni-a native 3D large language model
capable of understanding and generating 3D assets and text in any sequence.
First, we train a 3D vector-quantized variational autoencoder (VQVAE), which
maps 3D objects into a discrete latent space to achieve efficient and accurate
shape representation and reconstruction. Building upon the 3D-aware discrete
tokens, we innovatively construct a large-scale continuous training dataset
named 3D-Alpaca, encompassing generation, comprehension, and editing, thus
providing rich resources for future research and training. Finally, by
performing instruction-based training of the Qwen-2.5-vl-7B-Instruct model on
the 3D-Alpaca dataset. Our work provides an effective attempt at extending
multimodal models with basic 3D capabilities, which contributes to future
research in 3D-native AI. Project page:
https://github.com/JAMESYJL/ShapeLLM-Omni