ViDiC: Video Difference Captioning
Understanding visual differences between dynamic scenes requires the comparative perception of compositional, spatial, and temporal changes--a capability that remains underexplored in existing vision-language systems. While prior work on Image Difference Captioning (IDC) has enabled models to describe semantic changes between static images, these approaches fail to capture motion continuity, event evolution, or editing consistency over time. We introduce the ViDiC (Video Difference Captioning) task and its corresponding ViDiC-1K dataset, designed to evaluate the ability of Multimodal Large Language Models (MLLMs) to provide fine-grained descriptions of similarities and differences between video pairs. ViDiC-1K comprises 1,000 curated video pairs annotated with over 4,000 comparative checklist items, covering seven categories: subject, style, background, cinematography, motion, location, and playback techniques. To ensure reliable evaluation, we propose a dual-checklist framework that measures the accuracy of similarity and difference separately, based on the LLM-as-a-Judge protocol. Experiments on nineteen representative multimodal models reveal a significant performance gap in their comparative description and difference perception abilities. We hope ViDiC-1K can be a challenging benchmark that lays a solid foundation for advancing video understanding, edit awareness, and comparative reasoning in multimodal intelligence.
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Jiangtao Wu
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Shihao Li
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2025-12-04T08:43:59.201Z
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Shihao Li
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Zhaozhou Bian
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Yuanxing Zhang
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2025-12-04T10:41:05.482Z
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Yuanxing Zhang
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LongoXC
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Jialu Chen
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Runzhe Wen
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2025-12-04T10:41:12.901Z
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runzhe wen
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An Ping
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Yiwen He
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2025-12-04T10:41:19.015Z
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yiwen he
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Jiakai Wang
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2025-12-04T10:41:28.040Z
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Jiakai Wang
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Jiaheng Liu
ViDiC: Video Difference Captioning
Understanding visual differences between dynamic scenes requires the comparative perception of compositional, spatial, and temporal changes--a capability that remains underexplored in existing vision-language systems. While prior work on Image Difference Captioning (IDC) has enabled models to describe semantic changes between static images, these approaches fail to capture motion continuity, event evolution, or editing consistency over time. We introduce the ViDiC (Video Difference Captioning) task and its corresponding ViDiC-1K dataset, designed to evaluate the ability of Multimodal Large Language Models (MLLMs) to provide fine-grained descriptions of similarities and differences between video pairs. ViDiC-1K comprises 1,000 curated video pairs annotated with over 4,000 comparative checklist items, covering seven categories: subject, style, background, cinematography, motion, location, and playback techniques. To ensure reliable evaluation, we propose a dual-checklist framework that measures the accuracy of similarity and difference separately, based on the LLM-as-a-Judge protocol. Experiments on nineteen representative multimodal models reveal a significant performance gap in their comparative description and difference perception abilities. We hope ViDiC-1K can be a challenging benchmark that lays a solid foundation for advancing video understanding, edit awareness, and comparative reasoning in multimodal intelligence.
The ViDiC task and ViDiC-1K dataset evaluate Multimodal Large Language Models' ability to describe differences between video pairs, addressing limitations in capturing motion continuity and event evolution.
Image Difference Captioning Video Difference Captioning ViDiC ViDiC-1K Multimodal Large Language Models checklist items dual-checklist framework LLM-as-a-Judge protocol video understanding edit awareness comparative reasoning