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AI Shines Again! Chinese Creation Brings Out Mr. Bean + Tom and Jerry in a Parallel Universe Showdown

Honghao Wang

16 Nov 2025 — 4 min read

AI Brings Mr. Bean Into Tom and Jerry’s Living Room

Xinzhiyuan Report

Opening Scene: A Childhood Mash-Up

Mr. Bean sits awkwardly in Tom and Jerry’s iconic living room. Tom stumbles into a bucket of paint, Jerry smirks behind the couch.

This is not a parody — it's an AI-generated scene from a recent MBZUAI research paper that breaks through long-standing “style mismatch” problems in generative video.

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A Blast From Your Childhood

Tom could never catch Jerry.
Mr. Bean was endlessly clumsy.
The We Bare Bears brothers perpetually schemed.

These characters existed in separate worlds — cartoons vs. live-action comedies — never meeting, never mixing.

But now, AI has engineered realistic encounters among them without visual or stylistic incongruity.

Paper: https://arxiv.org/pdf/2510.05093

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From Style Clash to Seamless Coexistence

Previously, AI attempts to blend styles suffered from absurd crossovers:

> Mr. Bean rendered as a cartoon, Ice Bear depicted as human — breaking immersion completely.

The Breakthrough

Researchers developed Cross‑Character Embedding (CCE), enabling AI to learn:

Identity logic — capturing unique expressions, movement patterns, and behavioral rhythms.
Example: Mr. Bean’s awkwardness, Tom’s impulsiveness, Jerry’s cunning escapes.

By combining learned behavior embeddings, AI generates interactions that feel authentic and personality-consistent — all set in an AI-created “third world.”

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More Than Just Crossovers

This technique works beyond Bean × Tom, extending to scenarios like Panda × Sheldon.

Such world‑merging capability opens new opportunities for creators, supported by open-source platforms like AiToEarn官网 which can:

Generate AI content with reusable tools.
Publish across major platforms (Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X/Twitter).
Analyze performance and track AI模型排名.
Monetize global creativity efficiently.

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Understanding the Characters: CCE in Depth

CCE — Teaching AI the “Soul” of a Character

Traditional models:

Only recreate appearance from reference images.

In this study:

81+ hours / 52,000 clips from Tom and Jerry, Boonie Bears, Mr. Bean, Young Sheldon were labeled automatically by GPT‑4o in a structured `[Character] + action` format.

Example:

[Character: Mr. Bean], trips over a chair.
[Character: Jerry], laughs and hides behind the wall.

This separates identity vectors from behavior vectors, letting AI preserve expressive nuances.

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Repairing Style Confusion: CCA

CCA — Maintaining Visual Logic Between Worlds

Even if behaviors match, styles can break.

Cross-Character Augmentation (CCA) combats this by:

Segmenting characters with SAM2.
Re‑placing them into differently-styled backgrounds.
Balancing styles with just 10% cross-style composites in the training set.

Result:

Live‑action Mr. Bean in Tom and Jerry’s cartoon kitchen retains his real‑world look, while cartoon characters stay cartoon.

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Achieving Natural Multi-Character Interaction

The MBZUAI team tested 10 characters:

Cartoon: Tom, Jerry, Grizzly, Panda, Ice Bear
Live-action: Mr. Bean, Sheldon, Mary, George, Penny

Prompted AI to generate scenes with 2–3 interacting characters, maintaining style and identity.

Observers noted balanced rhythms — e.g., Tom restless, Ice Bear calm — creating genuine synergy.

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Benchmarks & Results

Implemented the first multi-character evaluation benchmark with 4 metrics:

Identity-P — Identity preservation
Motion-P — Motion consistency
Style-P — Style consistency
Interaction-P — Interaction naturalness

Outcome: Model leads all metrics compared to SkyReel‑A2 and Wan2.1, with human reviewers confirming true-looking interactions.

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From Character Mixing to World Mixing

The deeper shift:

AI not only merges characters — it aligns the rules of separate worlds:

Cartoon physics + live-action performance
Story timelines folded into one computational space

Potential applications:

Films unconstrained by copyright shooting limitations
NPCs with evolving memories & behavioral logic
Dynamic, AI-driven literature

Future AI will become a “multiverse director”, blending fiction and reality seamlessly — generating behaviors and relationships, not just visuals.

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References

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