Transform Movie Scenes Across Different Styles (Zootopia AI Project)

We transformed Zootopia's iconic sloth scene into 4 different movie styles using AI. The kids' reactions? They watched it 5 times and asked to do more.

AI Dad Lab

Published on

January 5, 2026

My kids wanted to see what the Zootopia DMV scene would look like in other movies.

We selected the iconic Flash the sloth scene. Three characters at a service counter. Simple composition. Perfect for AI transformation.

Using ChatGPT for prompts, Nanobana Pro for image generation, and Kling 01 for video, we created 4 complete transformations. Toy Story, Inside Out, Finding Nemo, and Moana. Each one maintaining the exact character positions and scene composition.

Avatar failed completely after 5 attempts. The Na'vi transformation showed hybrid animal features. Abandoned.

Total time: 90 minutes first attempt. Total cost: 240 Kling credits out of 3,000 monthly allocation. Kids watched the final stacked video 5 times.

This guide shows you the complete process. Exact prompts. Failed attempts. Credit costs. Step-by-step Premiere Pro settings. Everything you need to replicate this with your family.

Quick overview

Time: 90 minutes for first project (60 minutes for subsequent projects)
Cost: 240 Kling credits per project (8% of 3,000 monthly allocation)
Difficulty: Intermediate (expect multiple failed attempts at first)
Age range: 8-12 years
Key learning: Copy proven prompt structures instead of writing from scratch
What you'll create: 5-strip stacked vertical video showing original scene plus 4 movie style transformations

What you'll need

Tools:

ChatGPT (Free tier works, Plus $20/month recommended for faster responses)
Higgsfield with Nanobana Pro (subscription required)
Kling 01 (60 credits per 5-second video, 3,000 credits monthly)
Premiere Pro ($22.99/month) or CapCut Desktop (free)
Screen recording software to capture movie scene

Time investment:

Scene selection and prep: 10 minutes
Image generation with Nanobana: 30-45 minutes
Video generation with Kling: 25 minutes
Stacking in Premiere Pro: 15-20 minutes

Your child's input:

Scene selection (which movie to transform)
Movie style choices (which 4 styles to try)
Character replacement ideas

Parent skills:

Basic video editing (cropping, positioning)
Copy-paste prompts (no writing required)
File management and exports

Optional:

Music library for final edit
Social media accounts to share results

Step-by-step process

This workflow transforms one source scene into 4 different movie styles. Each transformation maintains exact character positions and composition.

Phase 1: Source scene selection and preparation (10 minutes)

Step 1: Choose your movie scene

Select a scene with 2-4 clearly defined characters. Service counters, tables, and doorways work best. Avoid action sequences or crowds.

I selected the Zootopia DMV scene. Judy and Nick at the counter. Flash the sloth behind it. Simple three-character composition.

Screen-record 10 seconds. Screenshot the first frame for image generation reference.

Step 2: Prepare video in Premiere Pro

Import your 10-second clip. Create new sequence with custom dimensions.

My settings:

File → New → Sequence
Horizontal: 1920
Vertical: 384 (this is 1/5th of 1080 for stacking)
Frame Rate: 30fps
Name: Zootopia Strip - Original

Drag clip into sequence. Adjust Position Y value to center characters in the thin strip. Focus on faces.

Export as MP4, H.264, 5 seconds. This becomes your base video for Kling transformations.

💡 Pro Tip: Creating the thin strip format (1920x384) FIRST means Kling outputs the same dimensions. Saves you from cropping 5 videos later.

Phase 2: Image generation with Nanobana Pro (30-45 minutes)

Step 3: Generate transformation prompts with ChatGPT

Upload your screenshot to ChatGPT. Use this prompt template for each movie style.

Can you write me a detailed prompt that I can give to Nanobana Pro, using the attached reference image, to transform this scene into the style of the [MOVIE NAME] movie?

This is a scene recreation task. I need to maintain the EXACT composition, camera angle, framing, and character positions from the reference image, but replace the characters with [MOVIE NAME] characters.

Character replacements:
- The rabbit character (left position, at the counter) should be replaced by [CHARACTER NAME]
- The fox character (center-left position, at the counter next to the rabbit) should be replaced by [CHARACTER NAME]
- The sloth character (right position, behind the counter, facing away) should be replaced by [CHARACTER NAME] shown from the back only

Important requirements:
- [CHARACTER 1] and [CHARACTER 2] should be standing AT the counter together (customer side), close to each other, with their heads at approximately the same height in frame
- [CHARACTER 3] should be behind the counter, completely facing away. We only see the back of their head and body
- The counter/desk setup should be maintained
- Transform the environment to [MOVIE SETTING]
- Complete character replacement. No hybrid or animal features. These should be 100% the actual [MOVIE NAME] characters
- Maintain exact spatial relationships and composition from the reference image

Result: ChatGPT generates a detailed 200-300 word prompt with exact positioning, environment details, and style specifications.

My movie choices:

Toy Story: Jessie, Woody, Rex in Andy's room with cloud wallpaper
Inside Out: Joy, Fear, Sadness at glowing control panel in Headquarters
Finding Nemo: Dory, Marlin, Sea turtle at underwater coral reef
Moana: Moana, Maui (with animated tattoos), Heihei rooster in tropical village

Step 4: Generate images in Nanobana Pro

For each movie style, go to Higgsfield and navigate to Nanobana Pro.

Upload your Zootopia screenshot. Paste the detailed prompt from ChatGPT. Click Generate.

Review results. Check for complete character replacement with no animal features. Verify exact positioning matches the original. Confirm the back-view character is facing away.

My iterations:

Toy Story: 2 generations (first was good)
Inside Out: 1 generation (perfect first try)
Finding Nemo: 3 generations (adjusted color saturation)
Moana: 2 generations (refined Maui's tattoo detail)
Avatar: 5 failed attempts, abandoned (characters showed hybrid features)

Download the best result for each style. Total time: 30-45 minutes for 4 successful transformations.

💡 Pro Tip: Get all images perfect BEFORE moving to video generation. Nanobana iterations are unlimited. Kling charges 60 credits per video.

Phase 3: Video generation with Kling 01 (25 minutes)

Step 5: Transform videos in Kling 01

Go to Kling website. Navigate to Kling 01. DO NOT use Motion Control (it rejects animated characters).

For each of your 4 movie styles, upload three things:

Video (required): Your 1920x384 Zootopia strip
Image: The corresponding Nanobana-generated transformation
Text prompt: Simplified version describing the transformation

Example prompt for Toy Story:

Transform this scene into Toy Story style. Jessie (cowgirl) and Woody (sheriff) at a desk in Andy's room. Rex the dinosaur behind desk facing away. Plastic toy aesthetic, cloud wallpaper, vibrant primary colors, warm bedroom lighting.

Settings:

Resolution: 1080p (maintains aspect ratio)
Duration: 5 seconds
Quality: Auto

Click Generate. Wait 5 minutes per video. Download when complete.

Credits used: 60 credits per 5-second video × 4 videos = 240 credits (8% of 3,000 monthly allocation)

Rename files: 02_ToyStory.mp4, 03_InsideOut.mp4, 04_FindingNemo.mp4, 05_Moana.mp4

Phase 4: Stacking videos in Premiere Pro (15-20 minutes)

Step 6: Create vertical Instagram sequence

File → New → Sequence
Horizontal: 1080
Vertical: 1920 (9:16 Instagram/TikTok ratio)
Frame Rate: 30fps
Name: Zootopia Stacked Reel

Step 7: Import all videos

Import your original Zootopia strip plus all 4 transformation videos.

Step 8: Stack vertically

Drag each video to separate video tracks (V1, V2, V3, V4, V5). Position each strip using Effect Controls panel.

Exact positioning values:

V1 (Bottom - Toy Story): Scale 56.25%, Position Y: 1728
V2 (Second - Inside Out): Scale 56.25%, Position Y: 1344
V3 (Middle - ORIGINAL ZOOTOPIA): Scale 56.25%, Position Y: 960
V4 (Fourth - Finding Nemo): Scale 56.25%, Position Y: 576
V5 (Top - Moana): Scale 56.25%, Position Y: 192

Step 9: Add labels (optional)

Create text graphics in left margin. Bold sans-serif font, white with black stroke. Label each strip with movie name.

Step 10: Export final reel

Format: H.264
Resolution: 1080 x 1920
Frame Rate: 30fps
Bitrate: 10-15 Mbps

Export as Zootopia_Stacked_Transformations_Final.mp4

What worked

Scene selection with clear character positions

Choosing the DMV scene was perfect. Three characters at defined locations. Customer service counter setup. Simple composition.

This translated across all movie styles. Counter became control panel (Inside Out), coral reef ledge (Finding Nemo), carved table (Moana), desk (Toy Story).

Lesson: Pick scenes with characters at fixed locations, not action sequences.

Detailed prompts with exact positioning

Adding comprehensive context at the beginning of prompts eliminated hybrid characters.

Starting with "This is a scene from Toy Story, NOT Zootopia" plus detailed position requirements (LEFT, CENTER-LEFT, RIGHT) gave clean replacements.

Lesson: Save your working prompt structure. Swap movie-specific details for future projects. This cut subsequent project time from 45 minutes to 15 minutes.

Using Kling 01 instead of Motion Control

Motion Control rejected the video with "No valid characters detected." Requires human or human-like characters.

Kling 01 multimodal generation accepted animated characters. Upload video plus image reference plus text prompt. Maintains original motion, transforms style.

Lesson: Always use Kling 01 for animated character transformations.

Thin strip format for stacking

Creating 1920 x 384 pixel strips (1/5th of 1920x1080) allowed perfect stacking into 9:16 vertical format.

Five strips stacked = 1080x1920 for Instagram/TikTok. Characters remained visible and recognizable.

Lesson: Create the thin strip in Premiere FIRST, then upload to Kling. Output maintains that aspect ratio.

What didn't work

Avatar transformation failed completely

Generated 5 images. All showed Nick and Judy with animal features visible. Blue skin mixed with fox and rabbit characteristics.

Why it failed: Na'vi are humanoid aliens. Transforming animal characters to humanoid aliens is too complex for current AI.

Time wasted: 20 minutes across 5 generation attempts

Lesson: Start with easier transformations (Toy Story, Inside Out). Avoid humanoid aliens or realistic humans.

Initial prompts created hybrid characters

First generations showed "Zootopia characters dressed as Toy Story characters." Fox features on Woody. Rabbit ears on Jessie.

Prompts started with "Transform this scene..." which implied modification, not replacement.

Time wasted: 20 minutes and several generation attempts

Lesson: Emphasize "complete character replacement" and "NO animal features" explicitly in prompts.

Motion Control rejection

Uploaded Zootopia video to Motion Control. Error: "No valid characters detected in the video."

Motion Control tracks human motion. Animated characters not supported.

Time wasted: 15 minutes trying different uploads

Credits wasted: 0 (rejections don't charge credits)

Lesson: Read tool requirements carefully. Kling 01 for animated characters, Motion Control for humans.

Premiere Pro black bars issue

Cropped video to thin strip in existing 1920x1080 sequence. Black bars appeared top and bottom.

Canvas size was still original dimensions. Content was cropped smaller.

Lesson: Create NEW sequence with exact dimensions (1920x384) BEFORE importing video. Don't crop in wrong-sized sequence.

Why this workflow works

The three-tool combination solves different problems.

ChatGPT generates detailed prompts with proper structure and positioning language. You don't need to know how to write prompts. Copy and swap movie-specific details.

Nanobana Pro creates high-quality style transformations with character replacement. Unlimited iterations mean you can refine until perfect before spending Kling credits.

Kling 01 maintains the original motion while applying the new visual style. This preserves timing, character movement, and scene dynamics.

Learning curve

First project takes 90 minutes. Expect failures. Avatar didn't work. Initial prompts created hybrids. Motion Control rejected the upload.

Second project takes 60 minutes. You have saved prompt structures. You know which tools to use. You understand the positioning requirements.

By the third project, you can complete transformations in 45 minutes.

Credit budget planning

Kling provides 3,000 credits monthly. Each 5-second video costs 60 credits.

One 5-strip project (4 transformations) uses 240 credits. That's 8% of monthly allocation.

You can create approximately 12 similar projects per month. Or 50 individual transformation videos.

Generate images first, refine until perfect, THEN commit to video generation. This prevents wasting credits on imperfect transformations.

Kids' engagement and reactions

Tested with 8, 10, and 12-year-olds. Immediate recognition of all movie styles. Excitement when spotting character transformations.

They watched the final video 5 times. Paused to examine each strip individually. Asked to watch it again the next day.

Follow-up questions: "Can we do this with other movies?" "Can I pick the scene next time?" "How does the AI know what Woody looks like?"

This became a discussion about AI training data, visual style consistency, and character design across different animation studios.

Getting the most out of these tools

Before you start:

Watch through the movie with your child to pick the scene together
Choose scenes with 2-4 characters (not crowds)
Look for service counters, tables, doorways (clear spatial relationships)
Screenshot and screen-record at the same time

During the process:

Generate all 4 images in Nanobana BEFORE moving to Kling
Review each image carefully for hybrid features before proceeding
Test stacking with one transformation before generating all four videos
Save your ChatGPT conversation to reuse prompt structures

What I'd do differently:

Skip Avatar entirely (humanoid aliens too complex)
Start with Toy Story (easiest transformation, distinct plastic aesthetic)
Create a template document with working prompts for future projects
Record the kids' reactions while watching for the first time

Platform comparison

Higgsfield Nanobana Pro

Pros: High-quality character replacements, unlimited iterations, good style matching
Cons: Requires subscription, learning curve for optimal prompts
Best for: Parents willing to experiment with multiple generations

Kling 01

Pros: Maintains original motion perfectly, accepts animated characters, 3,000 monthly credits
Cons: 60 credits per 5-second video, 5-minute generation time, can't preview before generating
Best for: Final video transformations after images are perfected

These two tools together create a reliable workflow. Nanobana for unlimited image refinement. Kling for video transformation only after images are perfect.

Common issues and solutions

Problem: Character replacements look hybrid (animal features mixed with target characters)

Solution: Add "This is a scene from [MOVIE], NOT Zootopia" at the beginning of your prompt. Include "Complete character replacement, no hybrid features" in requirements. Specify "NO animal features" for each character. Regenerate 2-3 times if needed.

Problem: Kling rejects your video with "No valid characters detected" error

Solution: You're using Motion Control instead of Kling 01. Motion Control requires human characters. Use Kling 01 multimodal generation for animated characters. Upload video plus image reference plus text prompt.

Problem: Character positions don't match the original scene

Solution: Your prompt doesn't specify positioning clearly enough. Use "EXACT CHARACTER POSITIONS" section. Specify LEFT, CENTER-LEFT, RIGHT positions explicitly. Include relative height ("heads at approximately the same height"). Mention spatial relationships ("close together," "next to each other").

Problem: Black bars appear when cropping video to thin strip

Solution: Create NEW sequence with exact dimensions first (1920x384). THEN import and position video. Don't crop in existing 1920x1080 sequence. Method: File → New → Sequence → Settings → Custom dimensions.

Problem: Third character (back view) shows too much detail or is facing forward

Solution: Emphasize "back view only" more strongly in prompt. Add "WE ONLY SEE THE BACK OF [CHARACTER]'S HEAD AND BODY" in all caps. Specify "completely facing away" and "turned completely away, working behind the counter."

Problem: Running low on Kling credits

Solution: Monthly allocation is 3,000 credits (50 five-second videos). Generate shorter clips for testing (3 seconds uses 36 credits). Refine all images in Nanobana BEFORE moving to Kling. Don't waste credits on imperfect image transformations. Calculate: 5-strip project with 4 transformations uses 240 credits (8% of monthly allocation).