多智能体协作+人类监督，让3D建模更准更美。
From Idea to Co-Creation: A Planner-Actor-Critic Framework for Agent Augmented 3D Modeling
- 分规划、执行、评审三角色，智能体自我反思迭代
- 错误率下降，模型精度与美观度显著提升
- 适合需要高质量3D模型的设计师和开发者
我们提出一种框架，通过多智能体自我反思与人机协同监督，扩展了经典的演员-评论家架构在创造性3D建模中的应用。现有方法依赖单提示智能体直接调用Blender MCP等工具执行命令，而我们的方案引入规划者（Planner）、执行者（Actor）和评审者（Critic）三者协同：规划者制定建模步骤，执行者操作，评审者提供迭代反馈，人类用户全程担任监督与指导角色。系统性对比显示，在多种3D建模场景中，该方法在几何精度、美学质量及任务完成率上均有提升。评估表明，由评审引导的自我反思结合人类监督，能有效降低建模错误，提升结果复杂度与质量，优于直接单提示执行。本工作证实，结构化智能体自省配合人类督导与建议，可在保持实时Blender同步与高效工作流的同时，生成更高品质的3D模型。
原文摘要 · Abstract (English)
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.
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