TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control
This addresses the need for high-quality text editing in image tasks to reduce resource costs, though it appears incremental in improving existing methods.
The paper tackles the problem of inaccurate stroke rendering and limited style controllability in text editing for images, achieving state-of-the-art performance with enhanced accuracy and fidelity.
In image editing tasks, high-quality text editing capabilities can significantly reduce both human and material resource costs. Existing methods, however, face significant limitations in terms of stroke accuracy for complex text and controllability of generated text styles. To address these challenges, we propose TextMaster, a solution capable of accurately editing text across various scenarios and image regions, while ensuring proper layout and controllable text style. Our method enhances the accuracy and fidelity of text rendering by incorporating high-resolution standard glyph information and applying perceptual loss within the text editing region. Additionally, we leverage an attention mechanism to compute intermediate layer bounding box regression loss for each character, enabling the model to learn text layout across varying contexts. Furthermore, we propose a novel style injection technique that enables controllable style transfer for the injected text. Through comprehensive experiments, we demonstrate the state-of-the-art performance of our method.