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DataMagic addresses the complexity of data video creation by introducing a declarative specification (DVSpec) that unifies charts, narration, and animations with data-bound references and declarative synchronization, ensuring data provenance and audio-visual alignment. A "Generate-then-Orchestrate" multi-agent strategy then optimizes narrative coherence by generating candidate scenes in parallel and orchestrating them globally. Evaluations demonstrate DataMagic significantly outperforms advanced LLMs in both quality and execution success rates, while a user study confirms improved creation efficiency and reduced cognitive load.
DataMagic achieves over 95% success rate and an 83% quality improvement in data video generation by declaratively orchestrating multi-agent scene creation, surpassing even state-of-the-art LLMs.
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: https://github.com/HKUSTDial/DataMagic.
