EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing
Keywords: 3D Scene Editing, Large Language Model, Diffusion-based Models
TL;DR: We propose a graph diffusion-based method fo language-guided 3D scene layout editing
Abstract: 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.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 12972
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