TextMaster: 一种通过字形-风格双控实现真实文本编辑的统一框架
TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control
AI总结：
TextMaster通过字形-风格双控机制，实现了高精度、可控的文本编辑，适用于多种场景和图像区域，提升了文本布局的准确性和风格的可控性。
摘要：
在图像编辑任务中，高质量的文本编辑能力可以显著降低人力和物质资源成本。然而，现有方法在处理复杂文本的笔画精度和生成文本风格的可控性方面存在显著局限。为了解决这些挑战，我们提出了TextMaster，一种能够准确编辑各种场景和图像区域文本，同时确保合理布局和可控文本风格的解决方案。我们的方法通过引入高分辨率标准字形信息并在文本编辑区域应用感知损失来提高文本渲染的准确性和保真度。此外，我们利用注意力机制计算每个性质层的边界框回归损失，使模型能够在不同上下文中学习文本布局。进一步，我们提出了一种新的风格注入技术，使注入的文本能够实现可控的风格迁移。通过全面的实验，我们展示了该方法的最先进性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
