Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding

2026-06-09

AI Summary

Existing text-driven foreground-conditioned outpainting methods often suffer from artifacts caused by semantic redundancy between the generated background and the foreground, compromising subject prominence and overall image quality. To address this issue, this work proposes CCE-Diffusion, a framework featuring a plug-and-play Customized Concept Embedding (CCE) module that precisely aligns generic noun semantics with specific visual instances. By integrating instance-aware loss and semantic-preserving prompt templates, the method effectively suppresses background artifacts without modifying the backbone diffusion model, enabling seamless integration into diverse foreground-conditioned outpainting systems. Experimental results demonstrate that CCE-Diffusion significantly reduces artifacts and enhances both generation quality and semantic consistency, as evidenced by comprehensive qualitative and quantitative evaluations.

Abstract

To showcase products, merchants often incur substantial costs creating high-quality display images. Foreground Conditioned Outpainting (FCO) meets this demand, allowing users to create desired backgrounds for foreground instances at a low cost by adjusting the text prompt. However, existing text-driven FCO methods exhibit critical flaws in their outputs, most notably the presence of artifacts, which refer to regions in the synthesized background that share the same semantics as the foreground instance. Such artifacts diminish the object's prominence and degrade image quality. We attribute the issue to the misalignment between the given instance and text-derived concept embeddings. To address this, we propose the Customized Concept Embedding Diffusion (CCE-Diffusion) framework. Its core is a CCE-Module to customize concept embeddings, bridging the gap between generic noun semantics and a specific visual instance. An Instance-Aware Loss guides the module's optimization, while a Semantic-Preserving Prompt Template prevents customized embeddings from distorting other words in the prompt. Both qualitative and quantitative evaluations demonstrate that CCE-Diffusion significantly reduces artifacts in the outputs. As a plug-and-play component, the CCE-Module can integrate with various FCO methods, enhancing their performance.

Problem

• Foreground Conditioned Outpainting
• artifacts
• text-instance alignment
• concept embedding
• image generation

Innovation

• Customized Concept Embedding
• Foreground Conditioned Outpainting
• Text-Instance Alignment
• Diffusion Model
• Artifact Reduction