Character IP Workshop • Building universes, not spokespersons

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Character IP Creation Through AI Production Systems

Building character universes used to require Hollywood budgets and years of production. Now? It's a pipeline problem. I've spent 19 documented steps (+more studies) figuring out how to achieve 90-100% facial consistency, multi-angle character libraries, and narrative frameworks that work across both realistic and absurdist styles.

Building IP Through AI-Driven Narratives

Watch how the same story premise unfolds through different characters and universes - demonstrating identity consistency, multi-angle generation, and cross-style narrative adaptation.

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Neo Tokyo Universe

Ziegler Universe

Explore This Approach Read the Research

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Let's Explore Building Character IP Together

Character IP Creation Through AI Production Systems

Building character universes used to require Hollywood budgets and years of production. Now? It's a pipeline problem. I've spent 19 documented steps figuring out how to achieve 90-100% facial consistency, multi-angle character libraries, and narrative frameworks.

Building IP Through AI-Driven Narratives

Let me introduce you to the concept through two parallel universes - same narrative arc, different characters, different visual styles. Watch how consistent character identity holds across wildly different storytelling approaches.

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Choose Story

Neo Tokyo

Ziegler

Explore This Approach Read the Research

The Democratization Moment

Generic AI video tools (Sora, VEO 3) give you motion, but not persistent identity. After seventeen steps of R&D, I've learned: if your character doesn't maintain identity consistency across angles, environments, and narrative styles - you're just making generic videos, not building IP.

This exploration focuses on character IP with identity lock - the same foundation Disney, Pixar, and international brands use for their franchises, now accessible to independent creators and emerging brands through AI.

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The Production Challenge That's Shifting

Grogu (Baby Yoda) required VFX teams, motion capture, and massive budgets to maintain character consistency across The Mandalorian's episodes. Hello Kitty built a $80B franchise through decades of careful brand management and licensing deals. Duolingo's Duo the Owl became iconic through years of consistent character deployment across platforms.

The bottleneck was never ideas - it was maintaining character identity across every angle, lighting condition, style, and narrative context. That consistency required massive teams, technical infrastructure, and production workflows that small creators couldn't access.

Now it's a systems problem. Through 19 documented (and more studies) production steps, I've built workflows for 90-100% facial consistency, multi-angle character libraries, cross-style transformations, and narrative frameworks. The question isn't "Can we afford this?" - it's "Do we understand the production systems?"

Technical Challenges in AI Character IP

The problems I've encountered building narrative-driven character branding with GenAI. Not claiming I've solved all of these - but I've made progress.

Identity Drift Across Platforms

Your character looks different on Instagram vs YouTube vs TikTok. Generic AI gives "a face," not "YOUR face" with consistent identity across social channels and narrative contexts.

No Narrative Framework

You have visuals but no story system for AI social branding. Character personality undefined, genre flexibility unknown, franchise potential unexplored.

Single-Style Brand Limitations

Stuck in one visual style for social content. Can't shift between realistic corporate and absurdist satire with the same character identity - limits narrative-driven branding range.

Temporal Coherence for Video

Movement looks synthetic in social media videos - flickering faces, drifting jawlines, eyebrow teleportation. No smooth evolution across frames for GenAI video branding.

Multi-Angle Inconsistency

Character only works face-forward. Profile views, 360° rotations, environment morphing all break identity - limits social media content variety.

IP Ownership for AI Branding

Who owns the AI-generated character? The model? The content? Licensing terms for GenAI social branding unclear, franchise rights undefined.

How This Exploration Usually Unfolds

Four phases that emerged from research. Think of this as a map, not a contract. You'll see what I've learned, try the systems, and decide what makes sense for your character.

Character DNA Discovery

Figure out personality matrix, visual identity specs (LPA layers), narrative hooks. Can they do realistic corporate AND absurdist satire? What's their genre flexibility?

Exploring: LPA Framework Patterns

Identity Model Experiments

Test custom LoRA training for 90-100% facial consistency. Generate multi-angle datasets (360° rotations). See if identity holds across lighting/environments.

Exploring: Training + Character Library

Narrative Framework Tests

Try building story arcs, scene templates, temporal consistency checks. Test scene generation at $0.011-0.035/image. See what narrative patterns emerge.

Exploring: Storyboarding + Generation

Style & Franchise Validation

Experiment with dual-axis control (realism 9 / absurdism 6). Run 360° identity tests with environment morphing. Explore franchise potential across platforms.

Exploring: Style Transformation

Reference Work

Examples from previous explorations. Coming soon.

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Hospitality launch sequence

Multilingual walkthrough scripted and produced for a resort reveal.

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Spanish promo variant

Persona voice and gestures adapted for a LATAM audience.

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Ops & iteration loop

How we keep avatars on-message with continuous improvement.

What You Might Walk Away With

Depends on how far you want to take it. Here's what I can share from the systems I've built.

Character DNA Package

LPA layer templates, personality framework, visual identity specs, narrative hooks, genre flexibility patterns I've tested

Custom Identity Model

Trained LoRA targeting 90-100% facial consistency - you own it, run it anywhere

Multi-Angle Dataset

360° reference library for testing identity consistency across views

Narrative Framework

Scene templates, temporal consistency tools, genre range examples (realistic ↔ absurdist)

Production Pipeline Access

Django commands I use, cost tracking sheets, quality validation checklist

Style Transformation Tools

Dual-axis realism/absurdism controls, the "photorealistic satire" formula

Franchise Playbook

IP expansion patterns I've observed, cross-platform adaptation notes, universe building sketches

Temporal Consistency Kit

Face reenactment research notes, coherence validation methods

Optional Areas to Explore

If the initial exploration goes well, there are deeper areas we could investigate together.

Franchise Expansion Experiments

Testing sequel characters, shared universe concepts, crossover narratives. Seeing if temporal consistency holds across multi-character scenes.

Absurdist Satire Calibration

Finding the right photorealism/absurdism balance for your audience. Testing comedic timing across narrative sequences. Corporate absurdism vs pure parody spectrum.

Cross-Platform Adaptation Tests

YouTube, Instagram, TikTok format experiments with character consistency. Vertical vs horizontal scene composition tests. Platform-specific pacing while maintaining identity lock.

Merchandise Asset Generation

Exploring 2D/3D asset generation for physical merchandise. Style sheets across product categories. Brand guideline packages if licensing becomes relevant.

Temporal Coherence Deep Dive

Advanced face reenactment for ultra-smooth motion. Per-frame identity experiments across long sequences. Micro-expression control research.

Story Generation at Scale

Automated narrative sequences with identity lock. 15-scene story arcs at ~$0.165 with V4 model. Temporal consistency validation across scenes. Batch generation cost optimization.

What's Negotiable, What's Not

Clear terms on what you own, what you control. Not interested in IP ambiguity.

IP Ownership (Non-Negotiable)

You own the character, the trained model, all generated content. I keep the right to show process/techniques publicly (anonymized). Licensing terms documented upfront.

Quality Standards (My Benchmarks)

I aim for 90-100% facial matching threshold. Multi-angle validation. Temporal coherence that doesn't flicker. If I can't hit these, I'll tell you.

Narrative Boundaries (Your Call)

You decide what stories/styles are on-brand. How far absurdism goes. Genre flexibility limits. Character evolution parameters.

Technical Transparency (Standard)

All code/commands documented. You can run everything independently. Pipeline cost breakdowns. Model architecture specs provided.

Cost Predictability

Per-scene pricing $0.011-0.035. Batch experiments negotiable. No hidden model training costs - you pay API costs directly.

Evolution Rights (Yours)

Character can age, change styles, cross into other franchises. You control derivative works. I'm just helping you build the foundation.

Interested in exploring this?

Let's have a conversation about your character. No commitment - just see if this approach makes sense for what you're building.

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