做数据视频，AI 终于不用靠猜了
做一条数据视频，过去要么你会写代码、懂剪辑，要么让 AI 直接生成但数字可能错。这篇把过程拆成两半：先让多个 AI 各自生成图表、旁白、动画的候选，再统一编排成一条叙事连贯的视频。关键在中间那层「声明式规范」，它把每个画面和背后的数据绑定，AI 改动画时不会把数字改错。在 109 个真实样本上，最强模型直接生成的质量只有 2.13/5，这套方法提到 3.89，用户做一条视频的时间也少了近八成。它不是你明天就能装上的工具，但「让 AI 干活、同时锁死数据准确性」这个思路，会是以后所有数据工具的方向。
📄 原文摘要(英文)
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.