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UESTC, 2vivo Mobile Communication Co. Ltd * Correspondence: lezhang@uestc.edu.cn, libra@vivo.com Abstract Custom Storyboard Generation (CSG) aims to produce high-quality, multi-character consistent storytelling. Current approaches based on static diffusion models, whether used in a one-shot manner or within multi-agent frameworks, face three key limitations: (1) Static models lack dynamic expressiveness and often resort to “copy-paste” pattern. (2) One-shot inference cannot iteratively correct missing attributes or poor prompt adherence. (3) Multi-agents rely on non-robust evaluators, ill-suited for assessing stylized, non-realistic animation. To address these, we propose AnimeAgent, the first Image-to-Video (I, V’s implicit motion prior to enhance consistency and expressiveness, while a mixed subjective-objective reviewer enables reliable iterative refinement. We also collect a human-annotated CSG benchmark with ground-truth. Experiments show AnimeAgent achieves SOTA performance in consistency, prompt fidelity, and stylization. AnimeAgent: Is the Multi-Agent via Image-to-Video models a Good Disney Storytelling Artist? Hailong Yan1,2, Shice Liu2, Tao Wang2, Xiangtao Zhang1
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Image-to-video models can now generate more consistent and expressive animated storyboards than static diffusion models, thanks to a Disney-inspired multi-agent framework.
