  Diffusion-based image generation models such as DALL-E 3 and Stable
Diffusion-XL demonstrate remarkable capabilities in generating images with
realistic and unique compositions. Yet, these models are not robust in
precisely reasoning about physical and spatial configurations of objects,
especially when instructed with unconventional, thereby out-of-distribution
descriptions, such as "a chair with five legs". In this paper, we propose a
language agent with chain-of-3D-thoughts (L3GO), an inference-time approach
that can reason about part-based 3D mesh generation of unconventional objects
that current data-driven diffusion models struggle with. More concretely, we
use large language models as agents to compose a desired object via
trial-and-error within the 3D simulation environment. To facilitate our
investigation, we develop a new benchmark, Unconventionally Feasible Objects
(UFO), as well as SimpleBlenv, a wrapper environment built on top of Blender
where language agents can build and compose atomic building blocks via API
calls. Human and automatic GPT-4V evaluations show that our approach surpasses
the standard GPT-4 and other language agents (e.g., ReAct and Reflexion) for 3D
mesh generation on ShapeNet. Moreover, when tested on our UFO benchmark, our
approach outperforms other state-of-the-art text-to-2D image and text-to-3D
models based on human evaluation.
