DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos

2026-08-30

AI Summary

为解决动态图表和数据视频中结构化叙述理解的问题，研究者通过DVBench基准测试评估多模态语言模型，并分析了不同模型的表现及特点。

Abstract

While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leaving a critical gap in understanding temporally evolving structured visual information. To address this gap, we introduce DVBench, a benchmark for evaluating MLLMs on data videos, a storytelling medium that integrates dynamic charts with structured narratives. We decompose data video understanding into five dimensions. DVBench comprises 300 real-world data videos and 1,000 human-verified QA pairs curated through a rigorous semi-automated pipeline. Extensive evaluations of nine MLLMs show that Gemini-3.1-Pro achieves the best overall performance, while Kimi-k2.5 is the strongest open-source model. We further identify two notable phenomena: open-source model performance does not scale strictly with parameter size, and narrative proficiency does not guarantee visual capability. Fine-grained analyses and ablation studies further reveal dimension-specific weaknesses and the effects of frame configurations and subtitle inputs, informing future MLLM development. DVBench is publicly available at https://bomiaowang.github.io/DVBench/.

Problem

• MLLMs
• dynamic charts
• data videos
• temporally evolving structured visual information
• narratives

Innovation

• DVBench
• data videos
• dynamic charts
• narrative proficiency
• MLLMs evaluation