Paper | Project Page | Youtube Video
Official implementation of "Text-Driven Image Editing via Learnable Regions"
Yuanze Lin, Yi-Wen Chen, Yi-Hsuan Tsai, Lu Jiang, Ming-Hsuan Yang
Sorry for delay, code will be released before 30th April!
Abstract: Language has emerged as a natural interface for image editing. In this paper, we introduce a method for region-based image editing driven by textual prompts, without the need for user-provided masks or sketches. Specifically, our approach leverages an existing pre-trained text-to-image model and introduces a bounding box generator to find the edit regions that are aligned with the textual prompts. We show that this simple approach enables flexible editing that is compatible with current image generation models, and is able to handle complex prompts featuring multiple objects, complex sentences, or long paragraphs. We conduct an extensive user study to compare our method against state-of-the-art methods. Experiments demonstrate the compet- itive performance of our method in manipulating images with high fidelity and realism that align with the language descriptions provided. Our project webpage: https://yuanze-lin.me/LearnableRegions_page.
@article{lin2023text,
title={Text-Driven Image Editing via Learnable Regions},
author={Lin, Yuanze and Chen, Yi-Wen and Tsai, Yi-Hsuan and Jiang, Lu and Yang, Ming-Hsuan},
journal={arXiv preprint arXiv:2311.16432},
year={2023}
}