🤖 AI

ViMax: Agentic Video Generation

ViMax is an agentic video generation framework that employs coordinated multi-agent collaboration, a hierarchical narrative engine, and dependency-aware visual consistency mechanisms to overcome the limitations of current methods in producing long-form, narrative-structured videos with sustained character and environmental coherence.

Original authors: Lingxuan Huang, Sizhe He, Hengji Zhou, Liqiang Nie, Lianghao Xia, Chao Huang

Published 2026-06-09

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Lingxuan Huang, Sizhe He, Hengji Zhou, Liqiang Nie, Lianghao Xia, Chao Huang

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you want to tell a long, complex story using video, like a short movie. Currently, most AI video tools are like talented but short-attention-span actors. They can make a beautiful 5-second clip of a person walking, but if you ask them to make a 10-minute movie where that same person walks through different rooms, talks to friends, and never changes their face or clothes, the AI gets confused. The character might suddenly have blue eyes instead of brown, or the kitchen might turn into a forest between scenes.

ViMax is a new system designed to solve this. Think of it not as a single actor, but as a highly organized film production company run by a team of specialized AI agents.

Here is how ViMax works, using simple analogies:

1. The "Director's Room" (Hierarchical Narrative Planning)

If you asked a single AI to write a whole movie script at once, it would get overwhelmed and forget the beginning by the time it reached the end.

The ViMax Solution: ViMax breaks the story down like a Russian nesting doll.
- First, it outlines the big "Events" (like the beginning, middle, and end of the movie).
- Then, it breaks those events into "Scenes."
- Finally, it breaks scenes into individual "Shots" (camera angles).
The Secret Sauce: To make sure the AI doesn't forget the big picture while focusing on small details, it uses a digital library (Retrieval-Augmented Generation). Every time the AI plans a new scene, it "looks up" the original story in its library to remind itself who the characters are and what the plot is about. This keeps the story consistent from start to finish.

2. The "Continuity Department" (Visual Consistency)

In real filmmaking, if a character wears a red hat in Scene 1, they must wear a red hat in Scene 10. If they are sitting on a blue couch, the couch stays blue. Current AI tools often forget these details.

The ViMax Solution: ViMax uses a dependency map (Graph-based Visual Dependency).
- Imagine a flowchart that connects all the shots. If Shot 5 needs to look like Shot 2, the system links them.
- When generating Shot 5, the AI doesn't just guess; it uses the actual image from Shot 2 as a reference. It's like an artist tracing over a previous sketch to make sure the character's face looks exactly the same.
The "Camera Move" Trick: To make sure a room looks the same from different angles (like looking at a character from the left, then the right), ViMax generates a transition video first. It creates a smooth video of the camera moving from one angle to the other, then cuts out the specific shots it needs from that smooth movement. This guarantees the 3D space (furniture, walls) stays consistent.

3. The "Quality Control Team" (VLM-Based Selection)

Sometimes, an AI might generate a video that looks okay but has a weird glitch, like a hand with six fingers.

The ViMax Solution: For every single shot, ViMax doesn't just make one video. It makes several candidates (like taking 5 different photos of the same pose).
Then, a "Judge" AI (a Vision-Language Model) looks at all of them and picks the best one that matches the script and looks the most realistic. This ensures that only the highest-quality clips make it into the final movie.

What Did They Prove?

The researchers tested ViMax on a new benchmark called ViMax-Bench, which challenges the AI to make videos with many shots (up to 13 shots in a row) while keeping characters and settings consistent.

The Result: ViMax outperformed existing methods (like Sora, Veo, and others) in keeping characters looking the same and the story making sense.
The Trade-off: It takes a bit more computing power and time because it's doing all this planning and checking, but the result is a much more coherent, longer video.

In Summary

ViMax is like upgrading from a solo improvisation act (where the AI makes things up as it goes and often forgets the plot) to a professional film crew (where a Director, Scriptwriter, and Continuity Manager work together to ensure the story flows logically and the actors look the same in every scene).

Limitations mentioned in the paper:

It relies on other powerful AI models to do the actual drawing and video making.
It can still struggle with very crowded scenes or complex interactions (like two people high-fiving).
It doesn't yet handle audio or dialogue synchronization.
There are ethical concerns about using such consistent video generation to create fake events or impersonate people, so the authors suggest it needs safety checks.

Technical Summary: ViMax - Agentic Video Generation

Problem Statement

Current video generation methods are fundamentally limited to short-form content, lacking the capacity for systematic narrative planning and long-term visual consistency. While recent models (e.g., Sora, Veo 3, HunyuanVideo) achieve high per-clip fidelity, they suffer from "catastrophic semantic forgetting" when extended to longer horizons. This results in unpredictable drifts in character identity, world geometry, and physical laws across shots, breaking narrative immersion. Existing approaches to mitigate these issues face significant trade-offs: streaming methods extend duration but fail at multi-scene narratives, while consistency mechanisms (e.g., StoryDiffusion) preserve subject identity but lack narrative planning. Furthermore, existing multi-agent frameworks decompose production roles but fail to effectively coordinate complex narrative, visual, and temporal constraints simultaneously.

Methodology

ViMax is an agentic framework designed to orchestrate long-form video production from conceptual ideas to fully rendered content. It employs a coordinated multi-agent workflow that decomposes the pipeline into specialized roles: screenwriting, shot planning, character styling, video generation, and VLM-based quality control.

1. Long-Form Narrative Planning

To manage the exponential complexity of extended narratives, ViMax utilizes a Hierarchical Story Decomposition strategy.

Recursive Decomposition: The system breaks down a complete screenplay into major story events, then into constituent scenes, and finally into detailed shot specifications. This prevents cognitive overload for language models by tackling appropriately scoped planning tasks at each level.
RAG-Enhanced Global Context: To counter information loss inherent in decomposition, ViMax integrates a Retrieval-Augmented Generation (RAG) system. It indexes the original source material (novel or screenplay) and dynamically retrieves relevant context (character motivations, plot connections) to enrich local planning decisions, ensuring thematic coherence across the entire timeline.

2. Visual Consistency Mechanisms

ViMax addresses visual drift through two complementary, dependency-aware mechanisms:

Graph-Based Visual Dependency: During the planning phase, the system constructs a dependency graph identifying shared visual elements (characters, environments, objects) across shots. During generation, it performs topological ordering of this graph. Dependent shots are generated using reference-conditioned generation (incorporating visual content from prerequisite nodes), while independent clusters are processed in parallel to maximize efficiency.
Spatial Coherence via Transition Videos: To ensure geometric consistency within scenes captured from multiple angles, ViMax generates transition videos that smoothly move the camera between viewpoints. Individual shots are then extracted from these transition videos, leveraging the video model's internal spatial coherence mechanisms to maintain consistent 3D geometry and layout.

3. Quality Control

The framework employs a VLM-based Quality Control mechanism using a "best-of-k" selection strategy. For each generation task, the system samples $k$ candidates in parallel. A Vision-Language Model (VLM) judge scores these candidates against shot planning requirements (visual fidelity, narrative consistency, specification adherence), selecting the highest-scoring candidate to proceed. This mitigates the variance of single-shot generation.

Key Contributions

Agentic Long-form Video Generation Framework: A multi-agent architecture that automates the creation of narrative video content by coordinating screenwriting, shot planning, character styling, and generation with VLM-based quality control.
Hierarchical Narrative Planning with Global Context: A novel approach combining hierarchical story decomposition with RAG, enabling language models to maintain long-range dependencies and thematic coherence across extended narratives.
Graph-based Visual Consistency: A dual-level mechanism utilizing graph-based dependency tracking for object consistency and transition video generation for spatial coherence, ensuring visual fidelity across multi-shot timelines.
ViMax-Bench: A new benchmark introduced to evaluate controlled cross-shot consistency challenges in long-form video generation.

Results

ViMax was evaluated on ViMax-Bench (multi-shot generation) and a novel-to-video adaptation task on NarrativeQA.

Visual Consistency: On ViMax-Bench, ViMax (using Gemini-3-Pro and Veo 3.1) achieved the highest Global Consistency (GC) score of 0.557, outperforming the strongest baseline (Veo 3.1 at 0.5005). Improvements were most pronounced in long-story groups, where baseline methods degraded significantly as scene boundaries increased.
Narrative Planning: In the novel-to-video task, ViMax achieved an average score of 4.11, surpassing the best baseline (Animaker at 3.83). It led across all fine-grained dimensions, including Character Behavior Consistency (CBC), Narrative Coherence (NC), and Narrative Faithfulness (NF).
Human Evaluation: In a pairwise preference study, ViMax achieved an average win rate of 74.76% against Veo 3.1 and 73.33% against IC-LoRA, particularly excelling in cross-scene consistency (CSC).
Ablation Studies: Removing the graph-based visual dependency caused a significant drop in global consistency (8.7% decrease), confirming the necessity of cross-shot references. Similarly, removing VLM-based quality control reduced consistency by 3.8%.
Efficiency: The graph-based scheduling allowed for parallel execution of independent shot clusters, achieving a superior quality-efficiency trade-off compared to strictly serial or fully parallel generation.

Significance and Limitations

The paper positions ViMax as a step toward fully agentic filmmaking, demonstrating that coordinated multi-agent workflows can automate the creation of coherent, extended visual narratives. By decomposing production into specialized agents and enforcing structural constraints via dependency graphs and RAG, ViMax addresses the core bottlenecks of long-form generation: narrative fragmentation and visual drift.

The authors acknowledge several limitations:

Dependency on Proprietary Models: The system relies on proprietary foundation models for reasoning and generation, making performance sensitive to model updates.
Evaluation Constraints: Automatic metrics rely on representation-based similarity, which may struggle with fine-grained narrative intent and complex interactions (e.g., multi-person contact).
Generative Brittleness: The system can fail in crowded scenes with complex interactions, and errors in keyframe generation can cascade downstream.
Scope: The current workflow does not address audio, dialogue synchronization, or interactive revision.

The paper concludes that while ViMax advances the state of automated content creation, future work may shift toward managing long-horizon visual memory across scenes, potentially leveraging capabilities native to increasingly powerful base models.