arXiv:2604.25220v2 Announce Type: replace
Abstract: Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication. Automatically generating them requires deciding what story to tell, designing effective visualizations, and producing executable animations synchronized with narration. Despite rapid progress in vision-language models (VLMs), it remains unclear how well they can perform this task from a high-level communicative intent, largely because no standardized benchmark exists. We introduce DATAREEL, a benchmark for automated data-driven video story generation containing 328 real-world data reels. Given a data table, a communicative intent, a target duration, and a style reference image, a model must generate executable animation code with synchronized subtitles, which we render and evaluate. Evaluating eight proprietary and open-weight VLMs reveals a substantial capability gap: open-weight models exhibit execution failure rates of up to 39.8%, whereas proprietary models achieve stronger overall performance. Yet even the best-performing models frequently generate static charts, subtitle-animation desynchronization, unstable layouts, and poor adherence to the reference style. We further introduce a strong agentic baseline that decomposes generation into planning, coding, and verification, consistently outperforming direct prompting in both human and automatic evaluations. The task remains far from solved; we release DATAREEL at https://github.com/vis-nlp/DataReel to support future work.
Abstract: Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication. Automatically generating them requires deciding what story to tell, designing effective visualizations, and producing executable animations synchronized with narration. Despite rapid progress in vision-language models (VLMs), it remains unclear how well they can perform this task from a high-level communicative intent, largely because no standardized benchmark exists. We introduce DATAREEL, a benchmark for automated data-driven video story generation containing 328 real-world data reels. Given a data table, a communicative intent, a target duration, and a style reference image, a model must generate executable animation code with synchronized subtitles, which we render and evaluate. Evaluating eight proprietary and open-weight VLMs reveals a substantial capability gap: open-weight models exhibit execution failure rates of up to 39.8%, whereas proprietary models achieve stronger overall performance. Yet even the best-performing models frequently generate static charts, subtitle-animation desynchronization, unstable layouts, and poor adherence to the reference style. We further introduce a strong agentic baseline that decomposes generation into planning, coding, and verification, consistently outperforming direct prompting in both human and automatic evaluations. The task remains far from solved; we release DATAREEL at https://github.com/vis-nlp/DataReel to support future work.
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