TextEditBench: Evaluating Reasoning-aware Text Editing Beyond Rendering
This addresses a fundamental gap in multimodal generation evaluation for researchers and developers, though it is incremental as it focuses on benchmarking rather than proposing new methods.
The paper tackles the problem of evaluating text editing in images, which requires generating legible characters while maintaining semantic, geometric, and contextual coherence, by introducing TextEditBench, a comprehensive benchmark that reveals current models struggle with context-dependent reasoning and physical consistency.
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.