Abstract
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.
Abstract (translated)
扩散基图像生成模型，如DALL-E 3 和 Stable Diffusion-XL，在生成具有真实感和独特组成的图像方面表现出非凡的能力。然而，这些模型在精确推理物体物理和空间配置时并不稳健，尤其是在使用不寻常的、非规范的指令时，从而导致分布外推。在本文中，我们提出了一个3D思考链（L3GO）语言代理，这是一种推理基于部分3D网格生成非规范物体的方法。具体来说，我们使用大型语言模型作为代理，在3D仿真环境中通过尝试和错误来合成所需对象。为了方便我们的研究，我们还开发了一个名为Unconventional Feasible Objects（UFO）的新基准，以及 SimpleBlenv，一个基于Blender的封装环境，其中语言代理可以通过API调用构建和组合原子构建块。人类和自动GPT-4V评估表明，我们的方法超越了标准GPT-4和其他语言模型（如ReAct和Reflexion）在ShapeNet上3D网格生成方面的标准。此外，当我们的UFO基准被测试时，我们的方法在其他基于人类评估的2D图像和2D-3D模型上均表现优异。
URL
https://arxiv.org/abs/2402.09052
