character-aging-progression

name	character-aging-progression
description	Generate visually consistent character images across different life stages using nano-banana-pro with age-specific prompts

Character Aging Progression

Create consistent character avatars from childhood through elderhood — maintaining identity while showing natural aging.

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Summary

Workflow for generating age-progression image sets that maintain character identity across 13 life stages (ages 3-75). Uses nano-banana-pro with reference face image for facial consistency plus age-specific descriptors for natural aging.

Validated: Alex avatar set — 13 ages × successful generations

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Life Stage Definitions

Age	Stage	Key Visual Markers
3	Early Childhood	Round face, large eyes relative to face, soft features
7	Middle Childhood	More defined features, baby fat reducing
13	Early Adolescence	Face lengthening, features sharpening
15	Mid Adolescence	Adult proportions emerging, youthful skin
18	Late Adolescence	Near-adult features, fresh complexion
21	Young Adult	Adult features fully formed, peak vitality
25	Established Adult	Features settled, confident bearing
30	Adult	Subtle maturity signs, professional demeanor
42	Middle Age	Fine lines beginning, distinguished look
55	Late Middle Age	Gray appearing, crow's feet, wisdom in eyes
62	Early Senior	More pronounced lines, fuller wisdom
68	Senior	Silver/white hair dominant, deep character lines
75	Elder	Weathered dignity, life experience visible

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Implementation Pattern

Model Selection

Recommended: google/nano-banana-pro — Best face consistency with reference image Alternative: google/nano-banana-2 — Faster generation (Gemini 3.1 Flash), same API surface

const AGES = [3, 7, 13, 15, 18, 21, 25, 30, 42, 55, 62, 68, 75];

async function generateAgeProgression(characterConfig, referenceImage) {
  const results = [];
  
  for (const age of AGES) {
    const prompt = buildAgePrompt(characterConfig, age);
    const result = await replicate.run("google/nano-banana-pro", {
      input: {
        prompt,
        image_input: [await toDataURI(referenceImage)],  // Array of data URIs (up to 14)
        aspect_ratio: "1:1",
        output_format: "png"
      }
    });
    results.push({ age, url: result });
  }
  
  return results;
}

Critical: Use image_input (array) not image (single string). Pass at least one reference image; more references improve face consistency.

Age-Specific Prompt Templates

const AGE_DESCRIPTORS = {
  3: "3-year-old toddler, round chubby cheeks, large curious eyes, soft baby features, innocent expression",
  7: "7-year-old child, youthful face beginning to mature, bright curious eyes, playful yet thoughtful",
  13: "13-year-old teenager, face lengthening into adult proportions, youthful energy, intelligent gaze",
  15: "15-year-old teen, near-adult features emerging, fresh complexion, confident yet developing",
  18: "18-year-old young adult, adult features forming, youthful skin, mature confidence emerging",
  21: "21-year-old young professional, adult features fully formed, peak vitality, sharp intelligent eyes",
  25: "25-year-old adult, established features, confident bearing, professional demeanor",
  30: "30-year-old adult, subtle signs of maturity, experienced gaze, professional polish",
  42: "42-year-old, distinguished appearance, fine lines beginning, wisdom visible in eyes",
  55: "55-year-old, gray hair appearing at temples, crow's feet, dignified bearing, deep experience",
  62: "62-year-old senior, more pronounced lines, fuller silver hair, warm wise expression",
  68: "68-year-old, silver/white hair dominant, deep character lines, experienced wise gaze",
  75: "75-year-old elder, weathered dignified face, white hair, life experience visible, gentle wisdom"
};

function buildAgePrompt(character, age) {
  const baseDesc = character.coreIdentity;  // "Alex Finch, intelligent features, analytical eyes"
  const ageDesc = AGE_DESCRIPTORS[age];
  const style = character.style || "professional portrait, soft natural lighting, clean background";
  
  return `${baseDesc}, ${ageDesc}, ${style}`;
}

---

Critical Success Factors

1. Core Identity Markers (Constant)

Define 3-5 unchanging traits that persist across all ages:

• Eye color and shape
• Facial structure (bone structure)
• Distinctive features (e.g., dimples, nose shape)
• Expression tendencies

const CHARACTER = {
  coreIdentity: "Alex Finch, sharp intelligent features, " +
    "distinctive analytical eyes, determined jawline, " +
    "characteristic half-smile",
  // These traits appear in EVERY age prompt
};

2. Age-Appropriate Styling

Avoid anachronisms — match style to age:

• Children: Casual, age-appropriate clothing
• Teens: Contemporary teen fashion
• Adults: Professional attire
• Seniors: Dignified, comfortable styling

3. Reference Image Selection

Choose a reference that shows:

• Clear facial features at neutral angle
• Good lighting without harsh shadows
• Direct or 3/4 view (not profile)
• Age 18-30 works best as reference anchor

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Cost Analysis

Set Size	Model	Cost	Time
13 ages	nano-banana-pro	$0.33	~7 min
13 ages	nano-banana-2 (1K)	$0.87	~4 min (faster)
13 ages × 2 variants	nano-banana-pro	$0.65	~14 min
Full persona set (63)	nano-banana-pro	$1.58	~35 min
Full persona set (63)	nano-banana-2 (1K)	$4.22	~20 min

Best Practice: Generate ages 21 and 42 first to validate consistency before full set.

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Quality Validation

Consistency Check

Compare across ages for:

1. Eye consistency — Same eye color/shape throughout
2. Feature evolution — Natural progression, not jarring changes
3. Identity preservation — Recognizably same person at all ages

Common Issues

Issue	Cause	Fix
Age jump too drastic	Age descriptors too different	Add intermediate transition language
Baby/child looks different	Reference face too adult	Use neutral young adult reference
Senior unrecognizable	Too much aging description	Reduce weathering descriptors

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File Organization

characters/
  {character-name}/
    reference/
      face-reference.png
    ages/
      Alex-03.png
      Alex-07.png
      ...
      Alex-75.png
    generation-report.json

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Integration with Avatar System

Alex's avatar system uses age progression for:

1. Birthday fallback — Show user's age-matched Alex
2. Persona defaults — Age 21 is default when no persona detected
3. Age-appropriate identity — Users see Alex at their life stage

// From avatarMappings.ts
export const AVAILABLE_AGES = [3, 7, 13, 15, 18, 21, 25, 30, 42, 55, 62, 68, 75];

export function findClosestAge(age: number): number {
  // Returns nearest available age from set
}

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Confidence Level

High — Empirically validated through:

• 13 age images generated for Alex character
• Consistent identity across full progression
• 768×768 optimized avatars deployed
• User birthday → age avatar mapping working