DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration

原文：DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration

作者：Yupeng Xie, Zhenyang Wang, Liangwei Wang, Jiayi Zhu, Zhouan Shen, Yuyu Luo

来源：arXiv cs.DB（数据库）

正文

Computer Science > Human-Computer Interaction

arXiv:2609.33403v1 (cs)

[Submitted on 27 Sep 2026]

Title:DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration

Authors:Yupeng Xie, Zhenyang Wang, Liangwei Wang, Jiayi Zhu, Zhouan Shen, Yuyu Luo

View a PDF of the paper titled DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration, by Yupeng Xie and 5 other authors

View PDF

HTML (experimental)

Abstract:Data videos communicate data insights through dynamic charts, voice narration, and synchronized animations, and have become a widely adopted form of data storytelling. However, producing them requires expertise in data analysis, narrative design, and video editing. Static visualization tools lack narrative and animation capabilities; authoring tools rely on pre-prepared charts rather than raw data; and pixel-level models generate videos end-to-end but cannot guarantee data accuracy or provenance. End-to-end automatic generation faces two core challenges: how to uniformly represent charts, narration, and animations together with their temporal relationships, and how to efficiently search a vast design space for narrative-coherent compositions. We present DataMagic, which authors data videos from raw tabular data through declarative multi-agent orchestration. First, the declarative specification DVSpec unifies charts, narration, and animations with data-bound references and declarative synchronization, ensuring data provenance and automatic audio-visual alignment. Second, a “Generate-then-Orchestrate” multi-agent strategy generates candidate scenes in parallel and then optimizes narrative coherence through global orchestration. DVSpec provides a shared state for three complementary interaction modes, bridging full automation with fine-grained human control. Evaluations on 109 real-world samples show that even the most advanced LLM (e.g., GPT-5) achieves only 2.13/5 with execution success rates between 48.62% and 86.24%; DataMagic improves quality to 3.89 (+83%) with success rates above 95%, with the most significant gains in animation and narrative dimensions. A user study shows that, compared to a conversational LLM workflow, DataMagic improves creation efficiency (79.7% reduction in task time) and reduces perceived cognitive load. Project page: this https URL.

Comments:

Accepted at IEEE VIS 2026

Subjects:

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Databases (cs.DB); Multiagent Systems (cs.MA)

Cite as:

arXiv:2609.33403 [cs.HC]

(or

arXiv:2609.33403v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.33403

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

主题

人机交互 · 人工智能 · 数据库 · 多智能体

由「前沿雷达」于 2026-09-27 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。