VideoVerse: 你的T2V生成器有世界模型能力来合成视频吗？
VideoVerse: Does Your T2V Generator Have World Model Capability to Synthesize Videos?
AI总结：
VideoVerse通过评估T2V模型对复杂时间因果关系和世界知识的理解能力，揭示现有模型与理想世界建模能力的差距。
摘要：
最近文本到视频（T2V）生成技术的快速发展使训练模型具备了更强的世界模型能力，使现有基准逐渐无法评估最先进的T2V模型。首先，当前评估维度如每帧美学质量和时间一致性已无法区分最先进的T2V模型。其次，事件级时间因果性——区分视频与其他模态的本质属性——仍 largely 未被探索。第三，现有基准缺乏对世界知识的系统评估，这是构建世界模型的关键能力。为解决这些问题，我们引入VideoVerse，一个专注于评估当前T2V模型是否能理解复杂时间因果性和世界知识以合成视频的综合基准。我们收集了跨不同领域的代表性视频，并提取其事件级描述，具有固有的时间因果性，然后由独立标注者重写为文本到视频提示。对于每个提示，我们设计了十个评估维度，涵盖动态和静态属性，最终得到300个提示、815个事件和793个评估问题。因此，通过使用现代视觉-语言模型开发了一个与人类偏好一致的基于问答的评估流程，系统地评估了领先的开源和闭源T2V系统，揭示了当前T2V模型与理想世界建模能力之间的差距。
英文摘要：
The recent rapid advancement of Text-to-Video (T2V) generation technologies are engaging the trained models with more world model ability, making the existing benchmarks increasingly insufficient to evaluate state-of-the-art T2V models. First, current evaluation dimensions, such as per-frame aesthetic quality and temporal consistency, are no longer able to differentiate state-of-the-art T2V models. Second, event-level temporal causality-an essential property that differentiates videos from other modalities-remains largely unexplored. Third, existing benchmarks lack a systematic assessment of world knowledge, which are essential capabilities for building world models. To address these issues, we introduce VideoVerse, a comprehensive benchmark focusing on evaluating whether the current T2V model could understand complex temporal causality and world knowledge to synthesize videos. We collect representative videos across diverse domains and extract their event-level descriptions with inherent temporal causality, which are then rewritten into text-to-video prompts by independent annotators. For each prompt, we design ten evaluation dimensions covering dynamic and static properties, resulting in 300 prompts, 815 events, and 793 evaluation questions. Consequently, a human preference-aligned QA-based evaluation pipeline is developed by using modern vision-language models to systematically benchmark leading open- and closed-source T2V systems, revealing the current gap between T2V models and desired world modeling abilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
