SAR3D: Autoregressive 3D Object Generation and Understanding via
  Multi-scale 3D VQVAE
Authors: name: Yongwei Chen, name: Yushi Lan, name: Shangchen Zhou, name: Tengfei Wang, name: XIngang Pan

Autoregressive models have demonstrated remarkable success across various
fields, from large language models (LLMs) to large multimodal models (LMMs) and
2D content generation, moving closer to artificial general intelligence (AGI).
Despite these advances, applying autoregressive approaches to 3D object
generation and understanding remains largely unexplored. This paper introduces
Scale AutoRegressive 3D (SAR3D), a novel framework that leverages a multi-scale
3D vector-quantized variational autoencoder (VQVAE) to tokenize 3D objects for
efficient autoregressive generation and detailed understanding. By predicting
the next scale in a multi-scale latent representation instead of the next
single token, SAR3D reduces generation time significantly, achieving fast 3D
object generation in just 0.82 seconds on an A6000 GPU. Additionally, given the
tokens enriched with hierarchical 3D-aware information, we finetune a
pretrained LLM on them, enabling multimodal comprehension of 3D content. Our
experiments show that SAR3D surpasses current 3D generation methods in both
speed and quality and allows LLMs to interpret and caption 3D models
comprehensively.