ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing

Video scene text editing aims to replace text appearing on scene surfaces in a video, such as storefront signs, whiteboards, and product labels, while preserving the surrounding content, motion, and camera dynamics. Although scene text editing has been extensively studied for still images, its video counterpart remains underdeveloped. Existing resources do not provide paired edits over real-world videos, current protocols rarely measure whether the edited region reads as the target string over time, and there is no large-scale open-source benchmark for systematic comparison. To close these gaps, we introduce ViTeX-Bench, a comprehensive benchmark suite for high-fidelity and temporally consistent video scene text editing. Specifically, we first built ViTeX-Dataset, containing 387 real-world 720p videos with precise text-region masks and editing instructions; the 230-training split provides the first such resource with paired editing results built through a semi-automatic pipeline, with the rest forming the evaluation set for standardized benchmarking. In ViTeX-Bench, we propose a three-axis evaluation protocol: text correctness, visual quality, and edit locality, which contains a total of 13 metrics that combine character-level OCR signals with motion and preservation metrics. Our benchmarking experiments indicate that all eight leading video editing models, including both public and commercial models, fail in distinct ways: they either flicker or drift over time, or simply fail to produce the requested editing effects. To further validate the efficacy of our proposed dataset, we trained ViTeX-Edit-14B on the paired training split with a motion-aligned glyph-video conditioning stream. We show that, trained on only 230 high-quality paired videos, ViTeX-Edit-14B achieves the strongest CharAcc among video-native editors (0.688, +11.1\% over VideoPainter) and leads three ranked temporal metrics, reducing the previous best by 2.1\% (Flicker$_f$), 6.6\% (Flicker$_c$), and 1.9\% (Warp$_c$), respectively.