# FilmWorld: Agentic Novel-to-Film Generation

> Source: <https://filmworld-ai.github.io/>
> Published: 2026-07-22 03:46:51+00:00

# Turning novels into films by

modeling a dynamic cinematic world

FilmWorld reframes narrative long-video generation as **Dynamic Cinematic World Modeling** — first **constructing** a stateful, persistent world from prose, then **evolving** it causally scene by scene.

1

**Huazhong University of Science and Technology**

2

**Wan Team, Alibaba Group**

## See FilmWorld in action

A quick look at films FilmWorld generates automatically from a short novel — here, *华强买瓜 · Huaqiang Buying Watermelons*, rendered in a few different visual styles. Click any to watch.

## A novel is not a longer clip — it is an evolving world

Video models excel at short, single-scene clips. A film demands dozens of scenes where every character, location, and prop carries state forward. Two habits break down at this scale.

#### Independent per-shot synthesis

Each shot is generated alone with no cross-shot state. A character injured in scene 3 reappears unharmed in scene 9 — identity and continuity simply are not tracked.

#### Sequential extrapolation

Shots are rolled out one after another with limited context. Semantic drift compounds over long narratives, and latency grows linearly, O(N), with shot count.

#### A persistent, evolving world

We build one cinematic world grounded in the novel and evolve it through plot-driven state transitions. Every shot is just a view rendered from that world at a moment in time.

## Dynamic Cinematic World Modeling

We formalize the cinematic world as a five-tuple. Every shot is a rendered view of this world; consistency becomes a structural guarantee rather than an emergent hope.

**Entity Set** Characters, locations, and props inferred and instantiated from the prose.

**State Space** Per-entity states: age, costume, emotion, weather, time of day, physical condition.

**State Identifier** A deterministic hash addressing each state to a reusable visual asset.

**Transition Function** Plot events drive causal state updates: W

t+1= 𝒯(W

t, p

t).

**Rendering Function** Synthesizes a shot from the active state: v

t= ℛ(W

t, d

t).

t→ W

t+1

**Character · Mathilde Loisel** state @ W₁

A plot event 𝒯(·, pt) advances the state; Φ maps it to a stable visual identity before ℛ renders the shot.

## Six specialized agent groups, two phases

Modeling a cinematic world spans heterogeneous skills. FilmWorld coordinates six agent groups across a Construction side and an Evolution side.

#### Narrative Structured Translation

Segments the novel, resolves aliases and pronouns into canonical entities, and recovers cinematic details prose leaves implicit.

#### World Entity State Modeling with Visual Anchoring

Discretizes states, hashes them into identifiers Φ, and anchors each first appearance to a reusable reference asset.

#### State-driven Shot Planning

Materializes the full state trajectory and shot directives, chaining each shot's opening to its predecessor's end-state.

**Parallelism through explicit state externalization.** Because the entire state trajectory is materialized symbolically before any pixel is drawn, rendering carries no sequential dependency — the runtime-dominant Evolution Phase becomes constant-latency in shot count, a Θ(N) theoretical speedup.

## See it on all 15 novels

Pick any source novel, read its opening in the original language or a translation, then compare the films inferred by FilmWorld against five agentic baselines.

### The Necklace

## How one novel becomes a world

Real assets from FilmWorld constructing *The Necklace*: entities are anchored to reference visuals, then states evolve into scene-level keyframes. Click any frame to enlarge.

## FilmEval — nine metrics, three dimensions

15 novels across easy, medium, and hard tiers, scored by an automated multimodal protocol that jointly reads the source novel and the generated film.

#### Cinematic Presentation

Film-level production quality: is the output a coherent, watchable cinematic artifact?

**Visual Presentation**— stability, plausibility, aesthetic appeal, style adherence.

**Narrative Expression & Pacing**— clarity of story, transitions, and rhythm.

**Audio-Visual Performance**— dialogue clarity, ambience, and synchronization.

#### Film Consistency

Do visual entities and environments stay coherent across shots and time?

**Character Consistency**— identity stability and story-role alignment.

**Scene Consistency**— stable, recognizable, separable locations.

**Object Consistency**— persistence and plausibility of salient props.

#### Novel Fidelity

Is the film faithful to the source — no fabrication, sound logic, full coverage?

**Hallucination Resistance**— avoids unsupported or conflicting content.

**Logic Reliability**— preserves causal, temporal, and relational logic.

**Story Restoration**— coverage of fine-grained source details.

## State of the art, stable across difficulty

Under a unified foundation stack and a shared Ghibli-anime style, FilmWorld leads every evaluation cell — and its advantage widens most on the hardest novels.

#### Overall FilmEval score

Six annotators rated all 90 films. FilmWorld ranks first in every difficulty–dimension cell, and the human ranking matches FilmEval exactly (Spearman ρ = 1.0).

| Method | Year | Easy | Medium | Hard | Overall | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| CP | FC | NF | CP | FC | NF | CP | FC | NF | |||
| MM-StoryAgent | 2025 | 81.53 | 84.40 | 78.49 | 77.47 | 81.22 | 74.44 | 80.47 | 87.70 | 78.88 | 80.51 |
| VGoT | 2025 | 83.27 | 86.93 | 76.82 | 80.33 | 87.72 | 82.37 | 80.00 | 87.99 | 72.74 | 82.02 |
| MovieAgent | 2025 | 77.27 | 84.28 | 85.88 | 78.93 | 87.07 | 86.62 | 81.73 | 89.43 | 84.63 | 83.98 |
| ViMax | 2026 | 83.13 | 87.66 | 78.48 | 76.27 | 85.94 | 78.61 | 78.73 | 87.60 | 73.55 | 81.11 |
| VideoClaw | 2026 | 82.27 | 86.15 | 84.22 | 81.67 | 88.34 | 82.15 | 80.53 | 88.12 | 84.28 | 84.19 |
| FilmWorld (Ours) | 2026 | 84.00 | 90.93 | 92.66 | 82.47 | 91.56 | 92.91 | 82.87 | 92.95 | 94.09 | 89.38 |

## BibTeX

```
@article{zuo2026filmworld,
  title={FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling},
  author={Zuo, Jialong and Zuo, Haotong and Zhang, Shiwei and Wang, Xiang and Li, Chen and Sang, Nong and Gao, Changxin and Bai, Xiang},
  journal={arXiv preprint arXiv:2607.19038},
  year={2026}
}
```