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.
View on arXiv@article{zheng2025_2410.12836,
title={ EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing },
author={ Kaizhi Zheng and Xiaotong Chen and Xuehai He and Jing Gu and Linjie Li and Zhengyuan Yang and Kevin Lin and Jianfeng Wang and Lijuan Wang and Xin Eric Wang },
journal={arXiv preprint arXiv:2410.12836},
year={ 2025 }
}