首个专为角色动画设计的评估基准，能更准确衡量视频生成模型的表现。
AnimationBench: Are Video Models Good at Character-Centric Animation?
- 基于动画十二原则和角色一致性设计可量化的评估维度。
- 实验证明其结果更贴近人类判断，能发现真实基准忽略的问题。
- 支持固定与开放两种评估模式，适合研究者定制化测试。
视频生成技术快速进步，但现有基准多针对真实视频设计，难以评估风格化动画生成中的角色一致性、夸张动作等特性。同时，这些基准依赖固定提示集和僵化流程，缺乏对开放领域内容和自定义评估的支持。为此，我们提出AnimationBench，首个系统性评估图像到视频动画生成的基准。该基准将动画十二基本原理和角色知识产权保护转化为可测量的评估维度，并引入语义一致性、动作合理性、镜头运动一致性等广义质量维度。支持标准化闭集评估以实现可复现比较，也支持灵活开集评估用于诊断分析，利用视觉-语言模型实现高效可扩展评估。大量实验表明，AnimationBench与人工判断高度一致，能揭示现实主义导向基准所忽视的动画特异性差异，从而对顶尖图像到视频模型提供更丰富、更具区分性的评估结果。
原文摘要 · Abstract (English)
Video generation has advanced rapidly, with recent methods producing increasingly convincing animated results. However, existing benchmarks-largely designed for realistic videos-struggle to evaluate animation-style generation with its stylized appearance, exaggerated motion, and character-centric consistency. Moreover, they also rely on fixed prompt sets and rigid pipelines, offering limited flexibility for open-domain content and custom evaluation needs. To address this gap, we introduce AnimationBench, the first systematic benchmark for evaluating animation image-to-video generation. AnimationBench operationalizes the Twelve Basic Principles of Animation and IP Preservation into measurable evaluation dimensions, together with Broader Quality Dimensions including semantic consistency, motion rationality, and camera motion consistency. The benchmark supports both a standardized close-set evaluation for reproducible comparison and a flexible open-set evaluation for diagnostic analysis, and leverages visual-language models for scalable assessment. Extensive experiments show that AnimationBench aligns well with human judgment and exposes animation-specific quality differences overlooked by realism-oriented benchmarks, leading to more informative and discriminative evaluation of state-of-the-art I2V models.
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