TextEditBench: 评估超出渲染的推理-aware文本编辑
TextEditBench: Evaluating Reasoning-aware Text Editing Beyond Rendering
AI总结：
TextEditBench 旨在评估文本编辑中超出渲染的推理能力，通过引入语义期望维度，推动多模态生成中的文本引导编辑技术发展。
摘要：
文本渲染最近 emerged 作为视觉生成中最具挑战性的前沿之一，引起了大规模扩散和多模态模型的广泛关注。然而，图像中的文本编辑仍 largely 未被探索，因为它需要在生成可读字符的同时保持语义、几何和上下文的一致性。为填补这一空白，我们引入了 TextEditBench，一个全面的评估基准，专门聚焦于图像中的文本中心区域。除了基本的像素操作外，我们的基准强调需要模型理解物理合理性、语言意义和跨模态依赖性的推理密集型编辑场景。我们进一步提出了一种新的评估维度，语义期望（SE），它衡量模型在文本编辑过程中维持语义一致性、上下文一致性和跨模态对齐的推理能力。在最先进的编辑系统上的大量实验表明，尽管当前模型可以遵循简单的文本指令，但它们在依赖上下文的推理、物理一致性和布局意识的整合方面仍然存在困难。通过将评估聚焦于这一长期被忽视但基础的能力，TextEditBench建立了新的测试平台，以推动文本引导的图像编辑和多模态生成中的推理发展。
英文摘要：
Text rendering has recently emerged as one of the most challenging frontiers in visual generation, drawing significant attention from large-scale diffusion and multimodal models. However, text editing within images remains largely unexplored, as it requires generating legible characters while preserving semantic, geometric, and contextual coherence. To fill this gap, we introduce TextEditBench, a comprehensive evaluation benchmark that explicitly focuses on text-centric regions in images. Beyond basic pixel manipulations, our benchmark emphasizes reasoning-intensive editing scenarios that require models to understand physical plausibility, linguistic meaning, and cross-modal dependencies. We further propose a novel evaluation dimension, Semantic Expectation (SE), which measures reasoning ability of model to maintain semantic consistency, contextual coherence, and cross-modal alignment during text editing. Extensive experiments on state-of-the-art editing systems reveal that while current models can follow simple textual instructions, they still struggle with context-dependent reasoning, physical consistency, and layout-aware integration. By focusing evaluation on this long-overlooked yet fundamental capability, TextEditBench establishes a new testing ground for advancing text-guided image editing and reasoning in multimodal generation.
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。
