ViMax: Agentic Video Generation
Quick Answer
ViMax introduces a novel agentic video generation framework that enhances long-form video creation through multi-agent collaboration, ensuring narrative coherence and visual consistency across scenes.
Quick Take
ViMax introduces a novel agentic video generation framework that enhances long-form video creation through multi-agent collaboration, ensuring narrative coherence and visual consistency across scenes. The system employs a hierarchical narrative engine and a dependency-aware mechanism to track character states, enabling the generation of extended narratives with improved storytelling integrity.
Key Points
- ViMax addresses limitations of current short-clip video generation methods.
- The framework integrates a hierarchical narrative engine for global story coherence.
- It features a dependency-aware mechanism for maintaining visual consistency.
- VLM-guided agents monitor narrative coherence and visual fidelity.
- Enables coordinated agent collaboration for extended narrative content generation.
Article Excerpt
From source RSS / original summaryarXiv:2606. 07649v1 Announce Type: new Abstract: Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide. Existing methods generate isolated sequences without narrative structure and lack mechanisms for maintaining character and environmental consistency across scenes.
We present ViMax, an agentic video generation framework that addresses video creation through coordinated multi-agent collaboration where specialized components negotiate narrative decisions, visual continuity, and production quality.
Our framework employs a hierarchical narrative engine with for global story coherence and a dependency-aware visual consistency mechanism that tracks character and environmental states across temporal boundaries, while VLM-guided agents continuously monitor and refine both narrative coherence and visual fidelity. The framework enables coordinated agent collaboration to generate extended narrative content.
This maintains both storytelling integrity and visual coherence across multi-scene timelines.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
A phase-aware LLM agent optimizes human-object interaction retrieval, outperforming Optuna TPE by 33.3% and VDTuner by 34.2% on the HICO-DET benchmark. This method enhances throughput by 15.3x over UniIR and demonstrates strong transferability across vector database management systems.