AniMaker: Automated Multi-Agent Animated Storytelling with MCTS-Driven
  Clip Generation
Despite rapid advancements in video generation models, generating coherent
storytelling videos that span multiple scenes and characters remains
challenging. Current methods often rigidly convert pre-generated keyframes into
fixed-length clips, resulting in disjointed narratives and pacing issues.
Furthermore, the inherent instability of video generation models means that
even a single low-quality clip can significantly degrade the entire output
animation's logical coherence and visual continuity. To overcome these
obstacles, we introduce AniMaker, a multi-agent framework enabling efficient
multi-candidate clip generation and storytelling-aware clip selection, thus
creating globally consistent and story-coherent animation solely from text
input. The framework is structured around specialized agents, including the
Director Agent for storyboard generation, the Photography Agent for video clip
generation, the Reviewer Agent for evaluation, and the Post-Production Agent
for editing and voiceover. Central to AniMaker's approach are two key technical
components: MCTS-Gen in Photography Agent, an efficient Monte Carlo Tree Search
(MCTS)-inspired strategy that intelligently navigates the candidate space to
generate high-potential clips while optimizing resource usage; and AniEval in
Reviewer Agent, the first framework specifically designed for multi-shot
animation evaluation, which assesses critical aspects such as story-level
consistency, action completion, and animation-specific features by considering
each clip in the context of its preceding and succeeding clips. Experiments
demonstrate that AniMaker achieves superior quality as measured by popular
metrics including VBench and our proposed AniEval framework, while
significantly improving the efficiency of multi-candidate generation, pushing
AI-generated storytelling animation closer to production standards.
684bad683b733ba3336870b6
Haoyuan Shi
claimed_verified
2025-06-13T07:38:52.713Z
652fb8bcc9dd2692a25ef2e3
/avatars/461e6cc1c3441cde18192b080b0b8576.svg
Haoyuan Shi
user
MrSunshy
684bad683b733ba3336870b7
Yunxin Li
extracted_pending
2025-06-13T04:47:39.539Z
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/avatars/bd03085995b1c34e0ac8a845cf2c4e83.svg
Yunxin Li
user
YunxinLi
684bad683b733ba3336870b8
Xinyu Chen
684bad683b733ba3336870b9
Longyue Wang
684bad683b733ba3336870ba
Baotian Hu
684bad683b733ba3336870bb
Min Zhang
AniMaker: Automated Multi-Agent Animated Storytelling with MCTS-Driven
  Clip Generation
Despite rapid advancements in video generation models, generating coherent
storytelling videos that span multiple scenes and characters remains
challenging. Current methods often rigidly convert pre-generated keyframes into
fixed-length clips, resulting in disjointed narratives and pacing issues.
Furthermore, the inherent instability of video generation models means that
even a single low-quality clip can significantly degrade the entire output
animation's logical coherence and visual continuity. To overcome these
obstacles, we introduce AniMaker, a multi-agent framework enabling efficient
multi-candidate clip generation and storytelling-aware clip selection, thus
creating globally consistent and story-coherent animation solely from text
input. The framework is structured around specialized agents, including the
Director Agent for storyboard generation, the Photography Agent for video clip
generation, the Reviewer Agent for evaluation, and the Post-Production Agent
for editing and voiceover. Central to AniMaker's approach are two key technical
components: MCTS-Gen in Photography Agent, an efficient Monte Carlo Tree Search
(MCTS)-inspired strategy that intelligently navigates the candidate space to
generate high-potential clips while optimizing resource usage; and AniEval in
Reviewer Agent, the first framework specifically designed for multi-shot
animation evaluation, which assesses critical aspects such as story-level
consistency, action completion, and animation-specific features by considering
each clip in the context of its preceding and succeeding clips. Experiments
demonstrate that AniMaker achieves superior quality as measured by popular
metrics including VBench and our proposed AniEval framework, while
significantly improving the efficiency of multi-candidate generation, pushing
AI-generated storytelling animation closer to production standards.
AniMaker, a multi-agent framework using MCTS-Gen and AniEval, generates coherent storytelling videos from text input, outperforming existing models with better quality and efficiency.
multi-agent framework Director Agent Photography Agent Reviewer Agent Post-Production Agent Monte Carlo Tree Search (MCTS) AniEval VBench action completion story-level consistency animation-specific features