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

TL;DR

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.

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.

Figures (11)

• Figure 1: Comparison of paradigms for CSG. Our CSG paradigm is inspired by the ($d$).
• Figure 2: Qualitative comparison of CSG methods across five criteria: High Quality, Consistency, Logical, Multi-Subjects, and Generalizability. Our method excels in visual fidelity, identity preservation, narrative coherence, and generalization — outperforming baselines that exhibit drift, incoherence, or stylistic inconsistency.
• Figure 3: Overview of AnimeAgent. The Director Agent builds a hierarchical textual dope sheet from user inputs. The Artist Agent generates a continuous motion trajectory using an I2V model (“Straight Ahead”). A Consistency Reviewer validates keyframes for identity and layout, enabling refinement, while a Subjective Critic selects the most expressive “Extremes” via “Pose-to-Pose” evaluation, producing a coherent and visually compelling storytelling.
• Figure 4: "Pose-to-Pose vs. Straight Ahead in Disney"
• Figure 5: Text-Guided Semantic Anchor First Frame.