An Efficient and Harmonized Framework for Balanced Cross-Domain Feature Integration

TL;DR

This paper introduces a novel framework for cross-domain image feature integration that improves content preservation and style coherence by leveraging customized models, attention modulation, and multi-model fusion techniques, outperforming existing methods.

Contribution

The paper presents a new framework utilizing customized models, fixed feature and adaptive attention fusion, and multi-model combinations to better balance content and style in image generation.

Findings

Outperforms state-of-the-art methods in content-style balance

Enhances content preservation through cross-model feature modulation

Achieves flexible multi-style fusion with spatial and temporal strategies

Abstract

Despite significant advancements in image generation using advanced generative frameworks, cross-image integration of content and style remains a key challenge. Current generative models, while powerful, frequently depend on vague textual prompts to define styles--creating difficulties in balancing content semantics and style preservation. We propose a novel framework that utilizes customized models to learn style representations. It enhances content preservation through cross-model feature and attention modulation, leveraging the inherent semantic consistency across models. Additionally, we introduce fixed feature and adaptive attention fusion to achieve the desired balance between content and style. We further develop spatial (mask-guided localized) and temporal (multi-style compositional) multi-model combinations, enabling flexible fusion of models and styles. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in balancing content preservation and stylistic coherence.