From Idea to Co-Creation: A Planner-Actor-Critic Framework for Agent Augmented 3D Modeling

TL;DR

This paper introduces a multi-agent, human-in-the-loop framework for 3D modeling that improves accuracy and quality over traditional single-prompt methods by incorporating structured reflection and supervision.

Contribution

It proposes a novel Planner-Actor-Critic architecture for 3D modeling that integrates self-reflection and human oversight, enhancing model quality and reducing errors.

Findings

Improved geometric accuracy and aesthetic quality.

Higher task completion rates in diverse scenarios.

Reduced modeling errors with critic-guided reflection.

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.