HuggingFace Daily Papers（社区热门论文）· HuggingFace Daily Papers（社区热门论文）·· 9 天前AI 评分43
DataMagic：通过声明式多智能体编排创作数据视频
AI 导读
DataMagic 通过声明式多智能体编排，从原始表格数据直接创作数据视频。其 DVSpec 规范统一图表、旁白与动画的数据绑定和同步关系，配合"先生成后编排"策略并行生成候选场景并优化叙事连贯性。在 109 个真实样本上，DataMagic 将质量从 GPT-5 的 2.13/5 提升至 3.89（+83%），成功率超过 95%，用户任务时间减少 79.7%。
正文
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 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yupeng Xie [view email]
[v1]
Sun, 27 Sep 2026 09:31:21 UTC (11,543 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org