AnimeAgent: Is the Multi-Agent via Image-to-Video models a Good Disney Storytelling Artist?

• URL: http://arxiv.org/abs/2602.20664v1
• Date: Tue, 24 Feb 2026 08:14:24 GMT
• Title: AnimeAgent: Is the Multi-Agent via Image-to-Video models a Good Disney Storytelling Artist?
• Authors: Hailong Yan, Shice Liu, Tao Wang, Xiangtao Zhang, Yijie Zhong, Jinwei Chen, Le Zhang, Bo Li,
• Abstract summary: AnimeAgent is an Image-to-Video (I2V)-based multi-agent framework for Custom Storyboard Generation.<n>Inspired by Disney's "Combination of Straight Ahead and Pose to Pose" workflow, AnimeAgent leverages I2V's implicit motion prior to enhance consistency and expressiveness.<n> Experiments show AnimeAgent achieves SOTA performance in consistency, prompt fidelity, and stylization.
• Score: 17.734200530216977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (I2V)-based multi-agent framework for CSG. Inspired by Disney's "Combination of Straight Ahead and Pose to Pose" workflow, AnimeAgent leverages I2V'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.

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