Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation

🌈 Abstract

The article proposes a diffusion-based approach for Text-to-Image (T2I) generation with interactive 3D layout control. The key points are:

• Existing layout control approaches are limited to 2D layouts, require static layouts beforehand, and fail to preserve generated images under layout changes, making them unsuitable for applications that require 3D object-wise control and iterative refinements.
• The proposed approach leverages depth-conditioned T2I models and introduces a novel approach for interactive 3D layout control, replacing 2D boxes with 3D boxes and revamping the T2I task as a multi-stage generation process.
• The approach uses a Dynamic Self-Attention (DSA) module and a consistent 3D object translation strategy to seamlessly add objects to the scene while preserving existing contents.
• Experiments show the approach can generate complicated scenes based on 3D layouts, outperforming depth-conditioned T2I methods and other layout control methods in object generation success rate and preserving objects under layout changes.

🙋 Q&A

[01] Introduction

1. What are the key limitations of existing Text-to-Image (T2I) diffusion models that the article aims to address?

• Existing T2I diffusion models struggle to accurately follow textual prompts, particularly in terms of object count, object placement, and understanding relationships between objects.

2. What are the key approaches the article introduces to address these limitations?

• The article proposes a diffusion-based approach for T2I generation with interactive 3D layout control.
• It leverages depth-conditioned T2I models and introduces a novel approach for interactive 3D layout control, replacing 2D boxes with 3D boxes and revamping the T2I task as a multi-stage generation process.
• The approach uses a Dynamic Self-Attention (DSA) module and a consistent 3D object translation strategy to seamlessly add objects to the scene while preserving existing contents.

[02] Related Work

1. What are the key limitations of existing approaches for 2D layout control in T2I diffusion models?

• Existing 2D layout control approaches are limited to 2D layouts, require the user to provide a static layout beforehand, and fail to preserve generated images under layout changes.
• They do not offer mechanisms to position objects in 3D, limiting controllability in applications that require 3D control over object location and orientation.

2. How do the authors address the issue of consistent object generation under layout changes?

• The authors propose a novel self-attention module and a strategy for consistent 3D translation to preserve object identity under layout changes, which is a limitation of existing approaches.

[03] Method

1. How does the proposed approach differ from existing 2D layout control approaches?

• The proposed approach revamps the T2I task as a sequential multi-stage generation process, where the user can interactively add, change, and move objects in 3D while preserving objects from earlier stages.
• It replaces the traditional 2D boxes used in layout control with 3D boxes, enabling 3D object-wise control.

2. What are the key technical components introduced in the proposed approach?

• Dynamic Self-Attention (DSA) module: Allows seamlessly adding objects to a scene while preserving the existing contents.
• Consistent 3D Translation strategy: Preserves the identity of objects under layout changes.

[04] Experiments

1. How does the proposed approach perform compared to the baseline LooseControl (LC) and the 2D layout control approach Layout-Guidance?

• The proposed approach outperforms LC and Layout-Guidance in object generation success rate and preserving objects under layout changes.
• It scores two times higher than LC and 15% higher than Layout-Guidance on Object Accuracy, demonstrating its effectiveness in executing the 3D layout.
• It also outperforms Layout-Guidance by a large margin on the mIoU metric, showing that generated objects are better enclosed within the layout boxes.

2. What are the key limitations and future work identified by the authors?

• The approach is sensitive to the aspect ratio of the 3D boxes, and large objects placed in small spaces can result in distortions.
• The object segmentation part is crucial for the Consistent 3D Translation strategy, and if it fails, it becomes difficult to preserve objects.
• The multi-stage generation pipeline adds computational overhead, but the authors argue it is a fair trade-off for the enhanced control over scene elements.
• Future work includes addressing the limitations and exploring ways to further improve the interactive 3D layout control and consistency.