T. Charaa1, T. Hamdeni2 and I. Abdeljaoued-Tej2, 1University of Carthage, Tunisia, 2University Tunis-El-Manar, Tunisia
Text-guided image editing on real images, particularly in the context of fashion, presents a highly versatile yet challenging task. This process requires that the editing system take as input only the original image and a textual instruction specifying the desired modifications. The system must au-tonomously identify the regions of the image to be altered while preserving the other characteristics of the original image. In this paper, we present our approach, which leverages state-of-the-art artificial intelli-gence techniques, including deep neural networks, large language models (LLMs), and advanced methods for image generation and editing, such as Stable Diffusion and InstructPix2Pix. By integrating these mod-els, our system achieves precise interpretation of textual instructions and ensures consistent application of modifications while maintaining the visual integrity and authenticity of the original image. This framework provides a comprehensive and scalable approach for text-guided image editing, applicable to fashion and various other domains.
Artificial Intelligence, Computer Vision, Image Editing, Neural Models, Text-Guided Image Editing, Deep Learning, Large Language Models (LLMs).
