Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback

TL;DR

This paper introduces LI3D, a system that uses large language models as interpreters for 3D scene layout, enabling natural language guided interactive 3D generation and editing with feedback mechanisms.

Contribution

The paper presents a novel system integrating LLMs with layout-to-3D models and visual feedback, advancing natural language guided 3D content creation.

Findings

Effective multi-round 3D editing via language interaction

LMMs demonstrate emergent spatial reasoning abilities

Enhanced visual quality through generative feedback

Abstract

Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space. Recent advancements in Large Language Models (LLMs) have demonstrated impressive reasoning, conversational, and zero-shot generation abilities across various domains. Surprisingly, these models also show great potential in realizing and interpreting the 3D space. In light of this, we propose a novel language-guided interactive 3D generation system, dubbed LI3D, that integrates LLMs as a 3D layout interpreter into the off-the-shelf layout-to-3D generative models, allowing users to flexibly and interactively generate visual content. Specifically, we design a versatile layout structure base on the bounding boxes and semantics to prompt the LLMs to model the spatial generation and reasoning from language. Our system also incorporates LLaVA, a large language and vision assistant, to provide generative feedback from the visual aspect for improving the visual quality of generated content. We validate the effectiveness of LI3D, primarily in 3D generation and editing through multi-round interactions, which can be flexibly extended to 2D generation and editing. Various experiments demonstrate the potential benefits of incorporating LLMs in generative AI for applications, e.g., metaverse. Moreover, we benchmark the layout reasoning performance of LLMs with neural visual artist tasks, revealing their emergent ability in the spatial layout domain.