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

arXiv: https://arxiv.org/abs/2601.05016 · HTML: https://mlsanity.com/paper/2601.05016/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code](https://mlsanity.com/paper/2403.01248/) (2024)
- [A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)](https://mlsanity.com/paper/2505.02279/) (2025)
- [BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement](https://mlsanity.com/paper/2412.14203/) (2024)
- [WorldCraft: Photo-Realistic 3D World Creation and Customization via LLM Agents](https://mlsanity.com/paper/2502.15601/) (2025)
- [DeepCritic: Deliberate Critique with Large Language Models](https://mlsanity.com/paper/2505.00662/) (2025)
- [A Survey On Text-to-3D Contents Generation In The Wild](https://mlsanity.com/paper/2405.09431/) (2024)
- [Gen3DEval: Using vLLMs for Automatic Evaluation of Generated 3D Objects](https://mlsanity.com/paper/2504.08125/) (2025)
- [CoherenDream: Boosting Holistic Text Coherence in 3D Generation via Multimodal Large Language Models Feedback](https://mlsanity.com/paper/2504.19860/) (2025)
- [Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation](https://mlsanity.com/paper/2508.00428/) (2025)

## Built on by (most cited first, 1 of 1)

- [Agentic Visual Generation: From Generative Models to Agentic Control](https://mlsanity.com/paper/2609.06758/) (2026)

## Abstract (arXiv)

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.