Turning Webtoon Panels into Short Animations: A Case Study on Image-to-Video AI Models

Background

At LETR WORKS, we’ve been exploring ways to expand the utility of webtoon content beyond static consumption. One intriguing idea was to bring static webtoon panels to life—transforming them into short, animated clips using recent advances in AI.

This project started as an early-stage exploration to test the potential of various Image-to-Video (I2V) models on webtoon-style illustrations. The goal: evaluate how well these models can animate webtoon images to create visually engaging motion content.

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Tech Stack Used

• Model Architecture: Based on Stable Diffusion
• I2V Models Tested:
  • SEINE
  • ConsistI2V
  • PIA
• Prompt Generation: LLAVA (image-to-text)
• Test Images: Webtoon panels from Return of the Mount Hua Sect and Tiger Brother

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Problem Definition

Can we generate short, dynamic animations directly from static webtoon panels?

Unlike typical photos, webtoons consist of stylized and sequentially connected illustrations. Adding subtle motion effects, camera zooms, or transitions could dramatically enhance engagement—especially for use in social media teasers or short-form video content.

However, the main challenges were:

• Most I2V models are trained on realistic photos or AI art—not webtoon-style images.
• Character motions and transitions are difficult to control.
• Prompt generation for each frame adds complexity to the pipeline.

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What We Tried

1. Basic Workflow:

• Input: Webtoon image
• Prompt: Auto-generated using LLAVA
• Output: 2–4 second video clip from each model

Example Input:
[Image 1 - Panel from Return of the Mount Hua Sect]

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2. Model Comparisons

SEINE

• Strength: Scene transitions between two images
• Result: Smooth, but limited to basic dissolve or horizontal slide effects
• Verdict: Works decently for simple panel transitions, but feels repetitive after a few uses

ConsistI2V

• Strength: Best overall performance for single-frame background motion
• Result: Maintained character structure and added gentle ripples or wind effects
• Weakness: Failed to animate characters directly
• Verdict: Most promising model tested

PIA

• Strength: Great for photo-realistic inputs
• Result: Completely distorted webtoon characters due to style mismatch
• Verdict: Not usable for comic or stylized art

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3. Prompt Automation with LLAVA

We used LLAVA to generate descriptive prompts like:
“Two warriors clashing with swords in a battlefield.”

Surprisingly, AI-generated prompts and manually written prompts performed almost identically in our tests, suggesting a good potential for automation.

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Summary of Results

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Lessons Learned & Next Steps

Key Takeaways:

• Current I2V models are not optimized for webtoon/comic style illustrations.
• Simple scene transitions or atmospheric animations (wind, water) are feasible.
• Prompt generation can be streamlined using vision-language models like LLAVA.

Next Steps:

• Build a custom training dataset using webtoon assets.
• Fine-tune I2V models for comic-like motion styles.
• Combine I2V with basic video editing tools to create hybrid semi-automated workflows.

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References

• ConsistI2V: https://github.com/TIGER-AI-Lab/ConsistI2V
• SEINE: https://github.com/Vchitect/SEINE
• PIA: https://github.com/open-mmlab/PIA
• LLAVA Project: https://llava-vl.github.io/

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