Character Generation Skill

Transforms image analysis into anime character artwork using Nano Banana Pro (gemini-3-pro-image-preview).

Key Features

•2K Resolution Output - High quality images suitable for production
•Reference Image Support - Uses source photo to maintain visual consistency
•Interleaved Output - Generates multiple images (full body + portrait) in a single API call
•Retry Logic - Handles rate limits with exponential backoff

When to Use

•After understand_image has analyzed a pet photo → Generate anime pet character
•After understand_image has analyzed a person photo → Generate anime alter-ego
•User explicitly requests character creation

⚠️ Critical: Single Subject Identification Across Multiple Images

When user provides 3+ reference images for character creation, each image may contain multiple subjects (e.g., two dogs, person with pet, etc.).

The key capability: Both Gemini 3 and Nano Banana Pro can reason across multiple images to identify the SAME entity appearing in all of them.

How It Works

code

Image 1: Golden retriever + person in background
Image 2: Golden retriever + another dog
Image 3: Golden retriever sleeping alone
                    ↓
    Gemini identifies: "The golden retriever appears in all 3 images"
                    ↓
    Character sheet generated for: ONE golden retriever

Implementation Requirements

•
Reference Image Analysis: When multiple images are provided, use Gemini 3's long context to:
- •Identify which subject appears CONSISTENTLY across all images
- •Distinguish between "main subject" vs "background entities"
- •Note visual characteristics that confirm same identity (color, markings, features)

•

Prompt Engineering: Include explicit instructions like:

code

"These reference images all contain the same [pet/person].
Identify the consistent subject across all images and generate
a character sheet for ONLY that subject, ignoring other entities
that may appear in individual images."

•
Generation Focus: When calling Nano Banana Pro:
- •Pass all reference images for consistency
- •Explicitly specify which subject to focus on
- •Request single-subject character sheet output

Anti-Pattern

code

❌ WRONG: Generate character from each subject in each image
   → Results in multiple characters when user wanted ONE

✅ RIGHT: Reason across images to find consistent subject
   → One character sheet for the entity that appears in ALL images

Inputs

Input	Type	Required	Default	Description
`analysis`	dict	Yes	—	Output from `understand_image` skill
`name`	str	No	Auto-generated	Character name
`style`	str	No	"anime"	Visual style (see options below)
`variants`	list	No	["full_body"]	Which views to generate

Style Options

Style	Description
`anime`	Standard anime style
`studio_ghibli`	Soft, painterly Ghibli aesthetic
`cyberpunk_anime`	Neon-lit, high-tech anime
`watercolor_anime`	Soft watercolor anime blend
`chibi`	Cute, super-deformed style

Variant Options

Variant	Description
`full_body`	Full body, standing pose
`portrait`	Head and shoulders
`expressions`	Expression sheet (happy, sad, angry, surprised)

Outputs

Output	Type	Description
`character`	Character	Character object with metadata
`images`	dict[str, Path]	Generated images by variant

Character Object Schema

python

Character(
    id="abc123",
    name="Mochi",
    source_type="pet",
    style="anime",
    analysis={...},      # Original analysis
    concept={...},       # Generated character concept
    generated_images={
        "full_body": Path("..."),
        "portrait": Path("...")
    }
)

Implementation Contract

python

class CharacterGenerator:
    async def generate_character_sheet(
        self,
        character: Character,
    ) -> dict[str, Path]:
        """
        Generate full character sheet (multiple views) in ONE API call.

        Uses Nano Banana Pro's interleaved output capability to generate
        both full_body and portrait images in a single request.

        Args:
            character: Character object with analysis/concept populated

        Returns:
            Dict mapping variant names to file paths:
            {
                "full_body": Path("..."),
                "portrait": Path("...")
            }
        """
        ...

    async def generate_character_image(
        self,
        character: Character,
        variant: str = "full_body",
    ) -> Optional[Path]:
        """Generate a single character image variant."""
        ...

Example Usage

python

from skills.generate_character import CharacterGenerator
from models.character import Character
from pathlib import Path

# Initialize
gen = CharacterGenerator()

# Create character with analysis and concept
character = Character(
    name="Mochi",
    source_image=Path("assets/demo_photos/pets/cat_01.webp"),
    source_type="pet",
    style="anime",
    analysis=pet_analysis,
    concept=pet_concept,
)

# Generate full character sheet (full_body + portrait)
images = await gen.generate_character_sheet(character)
# Returns: {"full_body": Path("..."), "portrait": Path("...")}

# Or generate a single variant
portrait_path = await gen.generate_character_image(character, variant="portrait")

Dependencies

•Nano Banana Pro API (image generation)
•understand_image skill (provides input)

Progressive Disclosure

Level	What User Specifies	What's Auto-Generated
0	Just `analysis`	Name, style, single full_body
1	`analysis` + `name` + `style`	Variants
2	Everything	Nothing

Error Handling

Error	Cause	Recovery
`ValueError`	Empty or invalid analysis	Run `understand_image` first
`APIError`	Nano Banana failure	Retry with backoff
`StyleError`	Unknown style	Use supported style