L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects (2402.09052v1)

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 LLMs 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.

• The paper introduces a novel chain-of-3D-thoughts methodology that integrates language reasoning with iterative 3D mesh refinement.
• It addresses diffusion models' spatial reasoning limits by generating unconventional objects from atypical prompts.
• Evaluation using the UFO benchmark and SimpleBlenv demonstrates L3GO’s superior performance over existing models.

Overview of L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects

The paper introduces L3GO, an innovative approach for the generation of unconventional 3D objects through LLMs. Positioned as an inference-time solution, L3GO seeks to address the limitations present in existing diffusion-based image generation models, notably those associated with the precise spatial configurations of objects under atypical prompts.

Key Contributions

1. Diffusion Model Limitations: The authors highlight the inherent weaknesses within models such as DALL-E 3 and Stable Diffusion-XL. These models, while accomplished in generating high-quality images, fall short in managing unconventional prompts like "a chair with five legs," especially due to their constraints in spatial reasoning.
2. Methodological Innovations: L3GO merges language-based reasoning with 3D modeling. This is achieved by leveraging LLMs to iteratively compose and refine 3D meshes in a simulated environment. The concept of "Chain-of-3D-thoughts" represents a foundational strategy to dissect the object creation process into manageable, iterative steps, integrating human feedback to ensure more precise outcomes.
3. Benchmark and Environment Development: The paper introduces the "Unconventionally Feasible Objects" (UFO) benchmark to evaluate the system's efficacy in crafting non-standard objects. Additionally, SimpleBlenv, an interface built on top of Blender, was crafted to enable seamless integration and testing of these language agents.
4. Evaluation Results: Extensive testing reveals that L3GO outpaces existing models, including GPT-4 and other language-based 3D mesh generation models, in generating objects from the ShapeNet dataset as well as the UFO benchmark. This edge is attributed to its structured, iterative refinement process that allows for more nuanced corrections.

Implications and Future Work

Practical Implications: The integration of LLMs with 3D modeling represents a novel frontier with profound implications. It offers potential applications across various fields including industrial design, education, and digital content creation. By providing enhanced spatial reasoning capabilities, L3GO can profoundly impact how unconventional designs are conceived and realized.

Theoretical Extensions: The authors propose that LLMs can bridge gaps in current 3D spatial modeling techniques. This proposition could pave the way for more comprehensive studies into how language and visual data interactions can enhance spatial reasoning.

Speculative Future Developments: The paper opens up avenues for future research to refine abilities of AI in unconventional object generation. Further exploration could delve into the fusion of more advanced LLM reasoning mechanisms with enhanced visual feedback loops, potentially resulting in even more sophisticated designs. The integration with more sophisticated models like ControlNet could also further refine textural and aesthetic quality.

Conclusion

L3GO stands as a promising advancement in AI-driven 3D modeling, setting a precedent for how LLMs can significantly improve the generation of unconventional objects. Its integration of structured language reasoning with iterative design approaches highlights a shift towards more intelligent, adaptable design frameworks in AI research. While there remain challenges, particularly in operational efficiency and mesh quality, L3GO represents a tangible step forward in bridging textual and spatial domains.