AniMaker: Automated Multi-Agent Animated Storytelling with MCTS-Driven
  Clip Generation
Authors: name: Haoyuan Shi; name: Yunxin Li; name: Xinyu Chen; name: Longyue Wang; name: Baotian Hu; name: Min Zhang
Published: 2025-06-12 10:06:21

Abstract: 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.