Enhancing Object Coherence in Layout-to-Image Synthesis: A Novel Approach with Global Semantic Fusion and Self-similarity Feature Enhancement

Core Concepts

Proposing a novel diffusion model with Global Semantic Fusion and Self-similarity Feature Enhancement to address object coherence challenges in layout-to-image synthesis.

Abstract

The content discusses the challenges of object coherence in layout-to-image synthesis and introduces a novel approach to address these challenges. It covers the use of semantic masks, diffusion models, and the integration of captions for semantic and physical coherence. The proposed model outperforms existing methods in image generation quality and controllability.

Stats

Our model outperforms previous SOTA methods on FID and DS by relatively 0.9, 3.3% on COCO-stuff, and 1.1 3.2% on ADE20K.
Code will be available at https://github.com/CodeGoat24/EOCNet.

Quotes

"Our model outperforms the previous SOTA methods on FID and DS by relatively 0.9, 3.3% on COCO-stuff, and 1.1 3.2% on ADE20K."

Key Insights Distilled From

Enhancing Object Coherence in Layout-to-Image Synthesis

by Yibin Wang,W... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2311.10522.pdf

Deeper Inquiries

How does the proposed Global Semantic Fusion module enhance semantic coherence in image synthesis

The Global Semantic Fusion (GSF) module plays a crucial role in enhancing semantic coherence in image synthesis by integrating semantic coherence requirements and layout restrictions into the image generation process. Unlike traditional methods that address layout restrictions and semantic coherence separately, GSF effectively merges these two aspects. By incorporating the supervision information from the layout embedding and caption embedding, GSF guides the image synthesis process to ensure that the resulting images reflect the intended semantic relationships within the objects. This comprehensive approach allows for a more nuanced control over semantic coherence, leading to more realistic and coherent images.

What are the implications of the Self-similarity Feature Enhancement module on physical coherence generation

The Self-similarity Feature Enhancement (SFE) module significantly impacts physical coherence generation in image synthesis. By leveraging the synergy between Rectified Cross Attention (RCA) and Self-similarity Coherence Attention (SCA), SFE enhances the model's ability to capture physical coherence relationships between objects in the layout. SCA explicitly integrates local contextual physical coherence restrictions into each pixel's generation process, ensuring that the generated images maintain realistic physical relationships between objects. This results in images with improved physical coherence, where objects align and interact naturally within the scene.

How can the concept of object coherence in image synthesis be applied to other domains beyond computer science

The concept of object coherence in image synthesis can be applied to various domains beyond computer science, offering valuable insights and applications. In fields like art and design, understanding and controlling object coherence can aid in creating visually appealing and harmonious compositions. In architecture, ensuring physical coherence between elements can lead to more functional and aesthetically pleasing designs. In storytelling and visual media, object coherence can enhance the narrative and visual storytelling by ensuring that the elements in the scene align with the intended message. Overall, the principles of object coherence can be utilized in diverse domains to improve the quality and effectiveness of visual communication and design.