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arXiv · 2610.13412

TextVBench: Evaluating Text Rendering and Preservation in Video Generation

Ruiqiang Zhang·Hengyi Wang·Chang Liu·Guanjie Wang·Zehua Ma·Weiming Zhang
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Abstract

Modern video generators can synthesize convincing scenes while rendering a few characters incorrectly, omitting requested text, or changing it across frames. Measuring these failures is harder than applying OCR independently to each frame: when a scene contains multiple text targets and multiple noisy OCR regions, the evaluator must first determine which observation belongs to which intended text. We introduce TextVBench, a benchmark and evaluation framework that formulates visual-text evaluation as a multi-target, multi-instance spatiotemporal correspondence problem. TextVBench contains two complementary tracks: TextVBench-T2V measures the creation and maintenance of prompt-specified text, while TextVBench-Ref2V measures preservation of text from reference images. Both tracks use target-conditioned candidate selection rather than unconstrained VLM transcription, support one target mapping to multiple OCR regions, and separate frame-level spatial fidelity from cross-frame temporal fidelity. A length-weighted harmonic score summarizes these factors, while extra-text diagnostics expose failure modes hidden by a single OCR score. The benchmark is organized around controlled text, layout, carrier, and motion attributes so that aggregate scores can be traced back to specific capability limits. Across the evaluated model runs, MiniMax-H3 reaches a T2V TextVScore of 68.32, while the strongest Ref2V score is 56.51; attribute slices reveal a pronounced drop as the number of simultaneous reference text regions increases.

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Ruiqiang Zhang, Hengyi Wang, Chang Liu, Guanjie Wang, Zehua Ma, Weiming Zhang. 2026-10-09. TextVBench: Evaluating Text Rendering and Preservation in Video Generation. https://arxiv.org/abs/2610.13412

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