TextVBench: Evaluating Text Rendering and Preservation in Video Generation

TextVBench is a new benchmark and evaluation framework for assessing how well video generation models render and preserve text. It introduces two tracks—TextVBench‑T2V for prompt‑specified text creation and maintenance, and TextVBench‑Ref2V for preserving text from reference images—using a multi‑target, multi‑instance spatiotemporal correspondence approach. The framework employs target‑conditioned candidate selection, supports multiple OCR regions per target, and separates spatial and temporal fidelity, summarizing results with a length‑weighted harmonic score and detailed diagnostics.

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• diffusion
• computer-vision
• multimodal
• benchmarks