CoTextor: Training-Free Modular Multilingual Text Editing via Layered Disentanglement and Depth-Aware Fusion

We introduce \textit{CoTextor}, a modular and training-free framework for multilingual text editing in images, designed to support human-AI co-creation through a user-controllable and reversible workflow. Unlike diffusion-based systems that operate as black boxes, \textit{CoTextor} separates the editing process into transparent layers—foreground extraction, background inpainting, semantic rewriting, and depth-aware reintegration—allowing precise user-guided operations such as rotation, translation, scaling, and warping. To ensure realism, we introduce a perceptually guided integration module that enhances photometric and geometric coherence during text reinsertion. Built entirely from publicly available pretrained components, \textit{CoTextor} is accessible to non-technical, multilingual users, requiring no retraining or annotation. Through real-world scenarios in poster localization, street art remixing, and educational content creation, we demonstrate how \textit{CoTextor} enables inclusive and expressive visual storytelling across cultural and linguistic contexts. Requests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with their co-authors prior to requesting a name change in the electronic proceedings. Use the "Report an Issue" link to request a name change. Do not remove: This comment is monitored to verify that the site is working properly