AI Fight Scene Generator

Generate a high-cut-density action / fight scene by first composing a 16-cell storyboard image, then driving Seedance 2.0 image-to-video off that storyboard.

Estimated credits: ~250 per run.

The core idea (from the AtlasCloud Seedance 2 + GPT Image 2 tutorial): action tension comes from cut density, not single-shot quality. Forcing the video model to follow a pre-drawn 4×4 storyboard grid gives you 16 distinct shots in a 15-second clip — landing punches, reverse angles, ECUs, whip-pans — that no t2v prompt could choreograph on its own.

Inputs

Name	Type	Required	Default	Description
`character_description`	text	yes	—	Full physical description of the fighter(s). Asymmetric details (eye colour, scar side, holster on left hip) help the model preserve identity across panels.
`environment_description`	text	yes	—	The scene setting — e.g. "cyberpunk wet back-alley, neon kanji signage, Stray-game aesthetic, rain on chrome."
`action_script`	text	yes	—	The action beat — prose or numbered beats. E.g. "Hero is cornered → blocks first punch → counter-elbow → throw opponent into trash cans → finisher."
`style_direction`	text	no	cinematic action film, anamorphic lens, high contrast, motion blur on hits	Aesthetic / look tags applied to every frame.
`duration`	int	no	15	Final video length in seconds. The storyboard's 16 cells map roughly 1 shot per second at default.
`aspect_ratio`	text	no	16:9	Output aspect — `16:9` cinematic, `9:16` vertical, `1:1` square.

Steps

Phase A — Character Sheet

Generate a clean turnaround-style character sheet using

muapi image generate

(model=

gpt-image-2-text-to-image

Prompt:

Character reference sheet of {{character_description}}. Three views — front, 3/4, profile — on a neutral grey backdrop. Studio lighting, full body, no text overlays, photoreal. Asymmetric identifying details preserved on the correct side. {{style_direction}}.

Aspect ratio:
```
3:2
```

Present the character sheet and confirm identity details look right before proceeding. This image becomes reference #1 for later phases.

Phase B — Environment Concept

Use

muapi image generate

(model=

nano-banana-2

) to design the scene/world:

Prompt:

Wide establishing shot of {{environment_description}}. No characters in frame — environment only. Strong perspective lines, depth, atmospheric haze. {{style_direction}}. Production-design concept art.

Aspect ratio:
```
{{aspect_ratio}}
```

Nano-Banana-2 is chosen here for its reasoning-driven composition — it's better than text-to-image-only models at producing locations with believable spatial logic (chokepoints, cover, sightlines) that an action scene can use. Present for approval. This becomes reference #2.

Phase C — 16-Cell Storyboard

Compose the action onto a single 4×4 storyboard image using

muapi image edit

(model=

gpt-image-2-image-to-image

Reference Images: the character sheet from Phase A and the environment plate from Phase B.

Prompt:

Compose a 4×4 storyboard grid (16 numbered cells) for the following action sequence:
{{action_script}}

CHARACTER (use reference image 1 identity throughout, asymmetric details preserved):
{{character_description}}

LOCATION (use reference image 2 spatial layout):
{{environment_description}}

Each cell labels: SHOT # (1–16) · SIZE (WIDE / MS / CU / ECU) · CAMERA-MOVE arrow (push, pull, whip, dolly, crash-zoom, handheld) · 1-word RHYTHM note (BEAT / IMPACT / RECOVERY / RESET).

Vary shot size aggressively — never two WIDEs in a row. Land every IMPACT on a CU or ECU.
Hand-drawn comic-book ink-and-wash style, monochrome with selective red accents on hits.
Numbered cells, clear gutters between panels.

Aesthetic: {{style_direction}}.

Aspect ratio:
```
1:1
```
(square works best for a 4×4 grid)

Present the storyboard to the user. Confirm:

The 16 shots read clearly
Identity stays consistent cell-to-cell
Cut density / shot-size variation looks aggressive enough

If a panel reads poorly, regenerate just the storyboard with that cell's note bolded ("CELL 7 must be an ECU on the right fist").

Phase D — Storyboard → Video (Seedance 2.0)

Hand the storyboard to

muapi video from-image

(model=

seedance-v2.0-i2v

Reference Image: the 16-cell storyboard from Phase C.

Prompt:

Generate a {{duration}}-second action sequence that strictly follows the 16-cell storyboard reference image, cell-by-cell, top-left to bottom-right.

- Honour each cell's labelled SHOT SIZE and CAMERA-MOVE — match cuts to the storyboard's rhythm notes.
- Strong cinematic feel and shot language. Exaggerated dynamics. Hits land hard with motion blur and impact frames.
- Camera language: anamorphic, handheld where the storyboard calls for it, locked-off where it doesn't.
- Native audio: impact sfx on every IMPACT cell, footsteps, fabric/Foley, restrained low score under the action.

Action being rendered: {{action_script}}.
Aesthetic: {{style_direction}}.

Duration:
```
{{duration}}
```
(default 15)
Aspect ratio:
```
{{aspect_ratio}}
```

After generation, present the final video. If the cut density feels too low or shots don't match the storyboard, regenerate Phase D first (cheaper than rebuilding the storyboard) with the prompt emphasising "strict cell-by-cell adherence" more aggressively.

Notes

Why the storyboard image and not a text storyboard? Seedance 2.0 i2v anchors its motion plan to the visual reference. A grid of 16 drawn cells gives it 16 visual targets to hit — text descriptions of shots get averaged into mush.
Asymmetric character details matter. Without something like "scar over the right eyebrow" or "leather glove on the left hand only", identity drift between cells is the #1 failure mode.
Use
seedance-2.0-i2v-480p
to draft. Cheaper preview pass before committing to the full-res
```
seedance-v2.0-i2v
```
run.
For longer fights, chain two runs: first run uses storyboard A (cells 1–16, beats 1–15s); second run uses storyboard B (cells 17–32, beats 15–30s) with the last cell of A as a continuity anchor in B's first cell.
Language: Both English and Chinese prompts work in all four models, so the storyboard cell labels can be in either language.

Trigger Keywords

fight scene

action sequence

storyboard to video

cut density

cinematic action

combat choreography

seedance 2 storyboard

Pipeline at a Glance

character_description ──► [GPT-Image-2 t2i]   ─► character sheet ──┐
                                                                    │
environment_description ─► [Nano-Banana-2 t2i] ─► environment plate ┼─► [GPT-Image-2 i2i] ─► 16-cell storyboard ─► [Seedance 2.0 i2v] ─► 15s action video
                                                                    │
action_script + style_direction ───────────────────────────────────►┘

Notes for the Executing Agent

This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call
```
muapi
```
CLI commands. Use
```
muapi auth configure
```
first if
```
MUAPI_API_KEY
```
is unset.

For model IDs without a CLI alias yet, fall back to the raw endpoint via

curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}'

and poll with

muapi predict wait <request_id>

Phase C uses TWO reference images (character sheet + environment plate). When calling
```
gpt-image-2-image-to-image
```
, pass them as a list under
```
images_list
```
(or the model's documented multi-ref field).
Substitute
```
{{input_name}}
```
placeholders with the user's actual inputs before issuing each call.

muapi-ai-fight-scene

NPX Install

Tags

SKILL.md Content

AI Fight Scene Generator

Inputs

Steps

Phase A — Character Sheet

Phase B — Environment Concept

Phase C — 16-Cell Storyboard

Phase D — Storyboard → Video (Seedance 2.0)

Notes

Trigger Keywords

Pipeline at a Glance

Notes for the Executing Agent