We present a framework that extends the Actor-Critic architecture to creative 3D modeling through multi-agent self-reflection and human-in-the-loop supervision. While existing approaches rely on single-prompt agents that directly execute modeling commands via tools like Blender MCP, our approach introduces a Planner-Actor-Critic architecture. In this design, the Planner coordinates modeling steps, the Actor executes them, and the Critic provides iterative feedback, while human users act as supervisors and advisors throughout the process. Through systematic comparison between single-prompt modeling and our reflective multi-agent approach, we demonstrate improvements in geometric accuracy, aesthetic quality, and task completion rates across diverse 3D modeling scenarios. Our evaluation reveals that critic-guided reflection, combined with human supervisory input, reduces modeling errors and increases complexity and quality of the result compared to direct single-prompt execution. This work establishes that structured agent self-reflection, when augmented by human oversight and advisory guidance, produces higher-quality 3D models while maintaining efficient workflow integration through real-time Blender synchronization.

翻译：我们提出了一种框架，通过多智能体自我反思和人在回路监督，将执行者-评判者架构扩展至创造性三维建模领域。现有方法依赖于通过Blender MCP等工具直接执行建模指令的单提示智能体，而我们的方法引入了规划者-执行者-评判者架构。在该设计中，规划者协调建模步骤，执行者实施操作，评判者提供迭代反馈，而人类用户在整个过程中担任监督者与顾问角色。通过对单提示建模与我们的反思型多智能体方法进行系统比较，我们在多样化的三维建模场景中证明了该方法在几何精度、美学质量和任务完成率方面的提升。评估结果表明，与直接的单提示执行相比，结合人类监督输入的评判者引导式反思能够减少建模错误，并提高生成结果的复杂度和质量。本研究表明：当结构化智能体自我反思机制辅以人类监督与咨询指导时，能够生成更高质量的三维模型，同时通过实时Blender同步保持高效的工作流集成。