EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing
Kaizhi Zheng Xiaotong Chen Xuehai He Jing Gu Linjie Li Zhengyuan Yang Kevin Lin Jianfeng Wang Lijuan Wang Xin Eric Wang
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.
3D Scene Editing, Large Language Model, Diffusion-based Models
We propose a graph diffusion-based method fo language-guided 3D scene layout editing