EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing

Given the steep learning curve of professional 3D software and the time- consuming process of managing large 3D assets, language-guided 3D scene editing has significant potential in fields such as virtual reality, augmented reality, and gaming. However, recent approaches to language-guided 3D scene editing either require manual interventions or focus only on appearance modifications without supporting comprehensive scene layout changes. In response, we propose EditRoom, a unified framework capable of executing a variety of layout edits through natural language commands, without requiring manual intervention. Specifically, EditRoom leverages Large Language Models (LLMs) for command planning and generates target scenes using a diffusion-based method, enabling six types of edits: rotate, translate, scale, replace, add, and remove. To address the lack of data for language-guided 3D scene editing, we have developed an automatic pipeline to augment existing 3D scene synthesis datasets and introduced EditRoom-DB, a large-scale dataset with 83k editing pairs, for training and evaluation. Our experiments demonstrate that our approach consistently outperforms other baselines across all metrics, indicating higher accuracy and coherence in language-guided scene layout editing.

Authors

• Kaizhi Zheng
• Xiaotong Chen
• Xuehai He
• Jing Gu
• Linjie Li
• Zhengyuan Yang
• Kevin Lin
• Jianfeng Wang
• Lijuan Wang
• Xin Eric Wang

Keywords

• 3D Scene Editing
• Large Language Model
• Diffusion-based Models