🤖 AI Summary
Existing single-prompt agents in 3D modeling lack planning and reflective capabilities, struggling to simultaneously achieve high geometric accuracy, aesthetic quality, and task completion rates. This work proposes a Planner-Actor-Critic multi-agent framework that introduces, for the first time, a structured self-reflection mechanism. By integrating real-time human guidance with the Blender MCP toolchain, the framework establishes a collaborative closed loop among planning, execution, and critique. This approach enables synchronous human-AI co-modeling and significantly outperforms baseline methods across diverse scenarios, demonstrating marked improvements in geometric precision, aesthetic quality, and task success rate, along with a substantial reduction in error frequency.
📝 Abstract
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.