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 & tables
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.
Figure 6: Layout-Robust Multi-Character I2V.
Methods
CSD ↑
CIDS ↑
PA ↑
CM ↑
Aes ↑
Cross
Self
Cross
Self
Scene
Shot
CI
IA
Avg.
StoryGen Liu et al. (2024)
0.371
0.531
0.417
0.568
1.26
2.29
1.42
1.71
1.67
50.8
4.02
TheaterGen Cheng et al. (2024)
0.184
0.392
0.348
0.578
2.76
1.81
0.93
1.06
1.64
55.4
4.90
StoryDiffusion Zhou et al. (2024)
0.340
0.547
0.436
0.565
2.00
3.45
2.64
2.68
2.69
57.4
5.13
Anim-Director Li et al. (2024b)
0.288
0.510
0.401
0.578
3.64
3.07
3.32
2.69
3.18
67.4
5.59
Story-Adapter Mao et al. (2024)
0.325
0.737
0.401
0.626
1.93
3.28
2.55
2.46
2.56
59.3
4.89
Table 1: Quantitative Results of Various CSG Methods on ViStoryBench Zhuang et al. (2025) . CI: Character Interaction, IA: Individual Action; The first , second , and third values are highlighted.
Figure 7: Identifying EXTREMENS via Motion Score.
Methods
CSD ↑
CIDS ↑
PA ↑
CM ↑
Aes ↑
Cross
Self
Cross
Self
Scene
Shot
CI
IA
Avg.
MOKI Meitu Inc. (2024)
0.214
0.694
0.372
0.621
1.88
0.94
2.88
1.10
1.70
46.0
5.79
MorphicStudio Morphic, Inc. (2024)
0.577
0.628
0.603
0.677
3.07
2.66
3.36
2.11
2.80
60.8
4.96
Aibrm MagicLight AI (2025)
0.412
0.730
0.557
0.740
3.05
2.53
3.56
2.05
2.80
75.5
5.72
ShenBi Maoyan Entertainment (2025)
0.275
0.575
0.418
0.585
2.81
3.45
3.79
2.72
3.19
61.3
5.07
Typemovie TypeMovie Team (2024)
0.325
0.646
0.464
0.621
3.00
2.61
2.86
2.30
2.69
74.1
5.32
Table 2: Quantitative Results of Various Commercial Platforms on ViStoryBench Zhuang et al. (2025) .
Methods
CSD ↑
CIDS ↑
PA ↑
CM ↑
Aes ↑
CLIP-I ↑
Cross
Self
Cross
Self
Scene
Shot
CI
IA
Avg.
StoryDiffusion Zhou et al. (2024)
0.422
0.485
0.443
0.461
2.26
2.77
2.59
2.45
2.52
52.6
5.14
0.615
Anim-Director Li et al. (2024b)
0.396
0.431
0.487
0.443
3.16
3.27
2.75
2.85
3.01
54.6
5.48
0.585
OmniGen2 Wu et al. (2025b)
0.515
0.628
0.627
0.591
3.45
2.97
2.83
2.72
2.99
58.9
5.23
0.704
FreeCus Zhang et al. (2025a)
0.474
0.538
0.531
0.576
2.95
2.86
2.44
2.52
2.69
55.3
5.08
0.662
Qwen-ImageEdit Wu et al. (2025a)
0.532
0.645
0.611
0.628
3.36
3.32
3.45
2.91
3.26
67.5
5.41
0.751
Table 3: Quantitative Results of Various CSG Methods on AnimeBoard-GT.
Figure 8: Qualitative Comparison Across Methods on Alice Story.
Figure 9: Qualitative Comparison Across Methods on Aladdin Story.
Figure 10: Qualitative Comparison Across Methods on Magic Instruction.
School of Information and Communication Engineering, Communication University of China, Beijing, China · State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China