# DataMagic: Transforming Tabular Data into Data Insight Video

arXiv: https://arxiv.org/abs/2606.20388 · HTML: https://mlsanity.com/paper/2606.20388/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering](https://mlsanity.com/paper/2405.07001/) (2024)
- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle](https://mlsanity.com/paper/2512.04324/) (2025)
- [nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoning](https://mlsanity.com/paper/2503.12880/) (2025)
- [IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation](https://mlsanity.com/paper/2601.04498/) (2026)
- [VisJudge-Bench: Aesthetics and Quality Assessment of Visualizations](https://mlsanity.com/paper/2510.22373/) (2025)
- [Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System](https://mlsanity.com/paper/1604.03583/) (2016)
- [Data Player: Automatic Generation of Data Videos with Narration-Animation Interplay](https://mlsanity.com/paper/2308.04703/) (2023)
- [VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing](https://mlsanity.com/paper/2605.15677/) (2026)
- [DeepEye: A Steerable Self-driving Data Agent System](https://mlsanity.com/paper/2603.28889/) (2026)
- [Data Playwright: Authoring Data Videos with Annotated Narration](https://mlsanity.com/paper/2410.03093/) (2024)
- [Reflecting on Design Paradigms of Animated Data Video Tools](https://mlsanity.com/paper/2502.04801/) (2025)
- [ChartMark: A Structured Grammar for Chart Annotation](https://mlsanity.com/paper/2507.21810/) (2025)
- [Debugging Defective Visualizations: Empirical Insights Informing a Human-AI Co-Debugging System](https://mlsanity.com/paper/2412.07673/) (2024)
- [T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables](https://mlsanity.com/paper/2508.19813/) (2025)
- [sketch-plot: Progressive Editing for Text-to-Image Academic Figures](https://mlsanity.com/paper/2606.09171/) (2026)
- [TableTale: Reviving the Narrative Interplay Between Data Tables and Text in Scientific Papers](https://mlsanity.com/paper/2602.22908/) (2026)
- [Provenance for Interactive Visualizations](https://mlsanity.com/paper/1805.02622/) (2018)

## Built on by (most cited first, 2 of 2)

- [DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration](https://mlsanity.com/paper/2609.33403/) (2026)
- [FinReportBench: Measuring and Improving Institution-Grade Financial Report Generation](https://mlsanity.com/paper/2608.04374/) (2026)

## Abstract (arXiv)

Data videos integrate dynamic charts, voice narration, and synchronized animations to communicate data insights as temporal narratives, making them an effective medium for improving data consumption efficiency in the data management lifecycle. However, producing high-quality data videos requires expertise spanning data analysis, narrative design, and video production. Existing approaches fall short: static visualization tools (e.g., BI dashboards) lack narrative logic and animation; authoring tools require users to pre-prepare visualizations rather than working from raw data; pixel-level video generation models cannot guarantee data fidelity or provenance. We demonstrate DataMagic, an end-to-end interactive system that transforms raw tabular data and natural language queries into narrative data-insight videos. To ensure data fidelity, DataMagic introduces the declarative specification DVSpec, which binds visual and animation elements to underlying data fields through data-driven semantic references. To address the combinatorial explosion of the design space, DataMagic adopts a Generate-then-Orchestrate multi-agent architecture that generates candidate scenes in parallel and then optimizes narrative coherence through global orchestration. Leveraging DVSpec's decoupling of logic and rendering, the system further supports three interaction modes and structured provenance-based data Q&A, transforming one-way videos into explorable interactive data interfaces. Evaluation on 109 real-world samples validates the effectiveness of the DataMagic. Homepage: https://datamagic-home.github.io/