3D-GPT: Procedural 3D Modeling with Large Language Models (2310.12945v2)

Abstract: In the pursuit of efficient automated content creation, procedural generation, leveraging modifiable parameters and rule-based systems, emerges as a promising approach. Nonetheless, it could be a demanding endeavor, given its intricate nature necessitating a deep understanding of rules, algorithms, and parameters. To reduce workload, we introduce 3D-GPT, a framework utilizing LLMs~(LLMs) for instruction-driven 3D modeling. 3D-GPT positions LLMs as proficient problem solvers, dissecting the procedural 3D modeling tasks into accessible segments and appointing the apt agent for each task. 3D-GPT integrates three core agents: the task dispatch agent, the conceptualization agent, and the modeling agent. They collaboratively achieve two objectives. First, it enhances concise initial scene descriptions, evolving them into detailed forms while dynamically adapting the text based on subsequent instructions. Second, it integrates procedural generation, extracting parameter values from enriched text to effortlessly interface with 3D software for asset creation. Our empirical investigations confirm that 3D-GPT not only interprets and executes instructions, delivering reliable results but also collaborates effectively with human designers. Furthermore, it seamlessly integrates with Blender, unlocking expanded manipulation possibilities. Our work highlights the potential of LLMs in 3D modeling, offering a basic framework for future advancements in scene generation and animation.

• The paper introduces a novel 3D-GPT framework that leverages large language models for instruction-driven procedural 3D scene generation.
• The paper details a multi-agent system that enhances scene descriptions and generates Python code to interface seamlessly with 3D software.
• Empirical results show high accuracy in large scene generation and improved parameter inference, demonstrating robust iterative 3D modeling capabilities.

Procedural 3D Modeling with 3D-GPT Framework

The paper "3D-GPT: Procedural 3D Modeling with LLMs" introduces a novel framework called 3D-GPT, which employs LLMs to facilitate instruction-driven procedural 3D modeling. This work highlights the integration of LLMs as problem-solving agents, leveraging their capabilities for planning, reasoning, and tool utilization in the field of 3D content creation. The approach is particularly focused on reducing the complexity inherent in procedural generation, which traditionally demands a detailed understanding of generation rules and algorithms.

3D-GPT consists of three core agents: the task dispatch agent, the conceptualization agent, and the modeling agent. These agents collectively achieve two primary objectives. First, they enhance the initial scene descriptions, dynamically adapting the text based on subsequent instructions, thereby providing enriched input for modeling software. Second, 3D-GPT employs procedural generation techniques to extract parameter values from text, allowing it to interface seamlessly with 3D software like Blender.

Core Contributions

The authors present several key contributions through their framework:

1. Instruction-driven 3D Scene Generation: 3D-GPT utilizes the inherent multimodal reasoning capabilities of LLMs to generate 3D scenes from natural language instructions efficiently. This method eliminates the need for traditional training, instead relying on the pre-trained knowledge embedded in LLMs to interpret instructions and guide 3D creation.
2. Python Code Generation for Real-World Applications: The framework explores an innovative path by generating Python code to control 3D software. This approach offers increased flexibility in real-world applications and demonstrates the adaptability of LLMs in directly interfacing with complex software environments.
3. Empirical Demonstrations: Through experiments, the paper demonstrates the alignment of 3D-GPT's outputs with user instructions in generating large scenes and complex objects. The results highlight the framework's capability in handling subsequential instructions, thus supporting iterative and interactive modeling processes.

Numerical and Comparative Insights

Empirical results underscore the efficiency and efficacy of the 3D-GPT framework. In tasks of large scene generation and fine-detail control for individual classes such as specific flower types, the framework consistently delivered accurate and diverse results. The framework's ability to perform detailed parameter inference showcases robust reasoning capabilities inherent in LLMs, even when the required information is not explicitly stated.

The ablation studies further reveal the importance of each agent within the multi-agent system. Notably, the Conceptualization Agent significantly enhances scene descriptions, resulting in improved CLIP scores, parameter diversity, and reduced failure rates. Meanwhile, the Task Dispatch Agent was crucial in ensuring effective planning and communication flow, particularly in handling subsequential instructions.

Implications and Future Directions

The introduction of 3D-GPT presents significant implications for both practical 3D modeling workflows and theoretical advancements in LLM applications. Practically, it paves the way for more efficient, user-friendly interfaces for designers, reducing the burden of parameter specification and enabling more intuitive creative processes. Theoretically, it expands the potential of LLMs as versatile problem-solving agents capable of bridging textual and visual domains without additional training.

Moving forward, potential research paths include enhancing curve and shading control, overcoming dependencies on procedural algorithms, and enabling multimodal instruction processing. Furthermore, fine-tuning LLMs for geometry control, enabling autonomous rule discovery, and processing diverse input types would push the boundaries of autonomous 3D modeling even further.

In conclusion, 3D-GPT represents a substantial step forward in leveraging LLMs for procedural 3D modeling, demonstrating a tangible intersection of natural language processing and computer graphics to foster more seamless and interactive design experiences.