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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.15299 |
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| _version_ | 1866908970884530176 |
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| author | Wu, Leyi Fang, Pengjun Sun, Kai Xing, Yazhou Wu, Yinwei Wang, Songsong Huang, Ziqi Zhou, Dan He, Yingqing Chen, Ying-Cong Chen, Qifeng |
| author_facet | Wu, Leyi Fang, Pengjun Sun, Kai Xing, Yazhou Wu, Yinwei Wang, Songsong Huang, Ziqi Zhou, Dan He, Yingqing Chen, Ying-Cong Chen, Qifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15299 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AnimationBench: Are Video Models Good at Character-Centric Animation? Wu, Leyi Fang, Pengjun Sun, Kai Xing, Yazhou Wu, Yinwei Wang, Songsong Huang, Ziqi Zhou, Dan He, Yingqing Chen, Ying-Cong Chen, Qifeng Computer Vision and Pattern Recognition 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. |
| title | AnimationBench: Are Video Models Good at Character-Centric Animation? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.15299 |