AnimationBench: Are Video Models Good at Character-Centric Animation?

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Image Processing · Depth: Advanced, quick

Summary

AnimationBench is introduced as the first systematic benchmark designed to evaluate image-to-video (I2V) generation specifically for character-centric animation. Existing video generation benchmarks, primarily focused on realistic video, are inadequate for assessing animation's stylized appearance, exaggerated motion, and character consistency. AnimationBench addresses this by operationalizing the Twelve Basic Principles of Animation and IP Preservation into measurable evaluation dimensions, alongside broader quality dimensions like semantic consistency, motion rationality, and camera motion consistency. It supports both standardized close-set evaluation for reproducible comparisons and flexible open-set evaluation for diagnostic analysis, utilizing visual-language models for scalable assessment. Experiments demonstrate that AnimationBench aligns well with human judgment and reveals animation-specific quality differences that realism-oriented benchmarks miss, providing more informative evaluations of current I2V models.

Key takeaway

For research scientists developing or evaluating image-to-video models for animation, you should integrate AnimationBench into your assessment pipeline. This benchmark offers a more nuanced and accurate evaluation of animation-specific qualities, such as character consistency and adherence to animation principles, which are often overlooked by realism-focused metrics. Utilizing AnimationBench will help you identify critical performance gaps and drive targeted improvements in your animation generation models.

Key insights

AnimationBench provides a specialized benchmark for character-centric animation I2V generation, addressing gaps in realism-focused evaluations.

Principles

• Animation evaluation needs character-centric consistency.
• IP preservation is a key animation evaluation dimension.
• Visual-language models enable scalable assessment.

Method

AnimationBench operationalizes animation principles and IP preservation into measurable dimensions, supporting both close-set and open-set evaluations, and uses visual-language models for scalable assessment.

In practice

• Use AnimationBench for animation I2V model comparison.
• Employ open-set evaluation for diagnostic analysis.
• Consider IP preservation in animation generation.

Topics

• AnimationBench
• Image-to-Video Generation
• Character Animation Evaluation
• Twelve Basic Principles of Animation
• Visual-Language Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.