What they did

The authors designed ViMax as a multi-agent system where different components handle narrative decisions, visual continuity, and production quality. The framework includes a hierarchical narrative engine that uses retrieval-augmented generation to ensure global story coherence across multiple scenes. A dependency-aware visual consistency mechanism tracks character appearances and environmental states over time, while VLM-guided agents monitor and refine both narrative and visual fidelity during generation. The system was evaluated on long-form video generation tasks requiring multiple coordinated scenes.

Key findings

• ViMax enables generation of extended narrative content with maintained storytelling integrity across multi-scene timelines.
• The dependency-aware visual consistency mechanism successfully tracks character and environmental states across temporal boundaries.
• Coordinated agent collaboration allows specialized components to negotiate visual continuity and production quality alongside narrative decisions.
• The framework integrates retrieval-augmented generation to support global story coherence beyond isolated sequence generation.

Why it matters

This work introduces a structured approach to long-form video generation that treats it as a multi-agent coordination problem rather than a sequence of isolated clips. By explicitly separating narrative planning, visual consistency, and quality monitoring into specialized agents, ViMax provides a framework for scaling generative video to extended narratives with maintained coherence.

Caveats

The paper does not report quantitative comparisons with baseline methods or standard benchmarks for long-form video generation. Qualitative results are not detailed in the abstract, and no specific metrics for narrative coherence or visual consistency are provided. The reliance on multiple interacting agents may introduce computational overhead not addressed here.