3D-GPT

3D-GPT is a specialized framework designed to bridge the gap between natural language processing and procedural 3D modeling. Unlike traditional Text-to-3D models that rely on NeRFs (Neural Radiance Fields) or Diffusion-based point clouds—which often result in 'melted' geometries—3D-GPT utilizes Large Language Models (LLMs) as intelligent agents to interpret complex text descriptions. It decomposes these descriptions into technical parameters and Python-based execution scripts designed for procedural engines like Blender. By 2026, the tool has evolved to support multi-modal inputs, allowing users to provide both text and sketches to guide the generation process. Its architecture consists of three core components: the Dispatcher (task decomposition), the Conceptualized Designer (parameter optimization), and the Modeling Tool (execution engine). This approach ensures that generated assets are not just visual shells but functional, manifold meshes with clean topologies suitable for gaming, architectural visualization, and industrial simulation. The 2026 iteration emphasizes local execution capabilities, reducing latency and ensuring data privacy for enterprise-level 3D workflows.