EZBlender: Efficient 3D Editing with Plan-and-ReAct Agent

Hao Wang; Wenhui Zhu; Shao Tang; Zhipeng Wang; Xuanzhao Dong; Xin Li; Xiwen Chen; Ashish Bastola; Xinhao Huang; Yalin Wang; Abolfazl Razi

arXiv · 2601.07143

EZBlender: Efficient 3D Editing with Plan-and-ReAct Agent

Hao Wang·Wenhui Zhu·Shao Tang·Zhipeng Wang·Xuanzhao Dong·Xin Li·Xiwen Chen·Ashish Bastola·Xinhao Huang·Yalin Wang·Abolfazl Razi

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Abstract

As a cornerstone of the modern digital economy, 3D modeling and rendering demand substantial resources and manual effort when scene editing is performed in the traditional manner. Despite recent progress in VLM-based agents for 3D editing, the fundamental trade-off between editing precision and agent responsiveness remains unresolved. To overcome these limitations, we present EZBlender, a Blender agent with a hybrid framework that combines planning-based task decomposition and reactive local autonomy for efficient human AI collaboration and semantically faithful 3D editing. Specifically, this unexplored Plan-and-ReAct design not only preserves editing quality but also significantly reduces latency and computational cost. To further validate the efficiency and effectiveness of the proposed edge-autonomy architecture, we construct a dedicated multi-tasking benchmark that has not been systematically investigated in prior research. In addition, we provide a comprehensive analysis of language model preference, system responsiveness, and economic efficiency.

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BibTeX RIS

Hao Wang, Wenhui Zhu, Shao Tang, Zhipeng Wang, Xuanzhao Dong, Xin Li, Xiwen Chen, Ashish Bastola, Xinhao Huang, Yalin Wang, Abolfazl Razi. 2026-01-12. EZBlender: Efficient 3D Editing with Plan-and-ReAct Agent. https://arxiv.org/abs/2601.07143

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