# Data Player: Automatic Generation of Data Videos with Narration-Animation Interplay

arXiv: https://arxiv.org/abs/2308.04703 · HTML: https://mlsanity.com/paper/2308.04703/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2023.

## Built on by (most cited first, 13 of 13)

- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [From Data to Story: Towards Automatic Animated Data Video Creation with LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2408.03876/) (2024)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions](https://mlsanity.com/paper/2503.04110/) (2025)
- [DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text](https://mlsanity.com/paper/2503.22946/) (2025)
- [DataMagic: Transforming Tabular Data into Data Insight Video](https://mlsanity.com/paper/2606.20388/) (2026)
- [Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective](https://mlsanity.com/paper/2503.02631/) (2025)
- [Do Language Model Agents Align with Humans in Rating Visualizations? An Empirical Study](https://mlsanity.com/paper/2505.06702/) (2025)
- [Speech-Synchronized Whiteboard Generation via VLM-Driven Structured Drawing Representations](https://mlsanity.com/paper/2603.25870/) (2026)
- [DATAREEL: Automated Data-Driven Video Story Generation with Animations](https://mlsanity.com/paper/2604.25220/) (2026)
- [Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data](https://mlsanity.com/paper/2402.03116/) (2024)
- [VoCa: Designing Speech-Canvas Interaction for Voice-Based Conversational Agents](https://mlsanity.com/paper/2610.04706/) (2026)

## Abstract (arXiv)

Data visualizations and narratives are often integrated to convey data stories effectively. Among various data storytelling formats, data videos have been garnering increasing attention. These videos provide an intuitive interpretation of data charts while vividly articulating the underlying data insights. However, the production of data videos demands a diverse set of professional skills and considerable manual labor, including understanding narratives, linking visual elements with narration segments, designing and crafting animations, recording audio narrations, and synchronizing audio with visual animations. To simplify this process, our paper introduces a novel method, referred to as Data Player, capable of automatically generating dynamic data videos with narration-animation interplay. This approach lowers the technical barriers associated with creating data videos rich in narration. To enable narration-animation interplay, Data Player constructs references between visualizations and text input. Specifically, it first extracts data into tables from the visualizations. Subsequently, it utilizes large language models to form semantic connections between text and visuals. Finally, Data Player encodes animation design knowledge as computational low-level constraints, allowing for the recommendation of suitable animation presets that align with the audio narration produced by text-to-speech technologies. We assessed Data Player's efficacy through an example gallery, a user study, and expert interviews. The evaluation results demonstrated that Data Player can generate high-quality data videos that are comparable to human-composed ones.