We present a novel approach for controllable, region-specific style editing driven by textual prompts. Building upon the state-space style alignment framework introduced by \emph{StyleMamba}, our method integrates a semantic segmentation model into the style transfer pipeline. This allows users to selectively apply text-driven style changes to specific segments (e.g., ``turn the building into a cyberpunk tower'') while leaving other regions (e.g., ``people'' or ``trees'') unchanged. By incorporating region-wise condition vectors and a region-specific directional loss, our method achieves high-fidelity transformations that respect both semantic boundaries and user-driven style descriptions. Extensive experiments demonstrate that our approach can flexibly handle complex scene stylizations in real-world scenarios, improving control and quality over purely global style transfer methods.
View on arXiv@article{li2025_2503.16129,
title={ Controllable Segmentation-Based Text-Guided Style Editing },
author={ Jingwen Li and Aravind Chandrasekar and Mariana Rocha and Chao Li and Yuqing Chen },
journal={arXiv preprint arXiv:2503.16129},
year={ 2025 }
}