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The Current Limitations of AI in Animation

As businesses increasingly turn to animated videos to engage audiences and convey their brand messages, the potential role of artificial intelligence (AI) in the animation industry has generated significant interest. While generative AI has made considerable advancements, it still faces numerous limitations in animation.

What is Generative AI?

AI generated animation of a crossing guard holding a stop sign
"A crossing guard holding up a stop sign"- Generated with Runway Gen-2

Generative AI refers to artificial intelligence capable of creating new content or data, such as images, text, music, or other forms of media. These AI models learn patterns, features, and structures from existing data and use that knowledge to generate new, unique examples. The main goal is to create original, realistic, and meaningful outputs, often mimicking the style or characteristics of the training data.


1. Quality Control

A primary concern for businesses seeking animated videos is the quality of the final product. AI-generated animations often struggle to maintain a consistent level of detail, texture, and polish compared to human-made animations. Ensuring consistency in quality remains a significant challenge for AI.

2. Bias

Companies must be mindful of biases and stereotypes in their marketing materials. AI algorithms, however, may inadvertently perpetuate such biases within generated animations due to their training data. Addressing these biases is essential to ensure fair and inclusive representation in AI-generated content, aligning with a company's commitment to diversity and inclusion.

3. Computational Requirements

Creating AI-generated animations can be resource-intensive, necessitating substantial computational power. This high demand for resources may limit the adoption of AI-generated animations, particularly for small businesses or independent creators who may not have access to the necessary infrastructure.

4. Explainability and Interpretability

Understanding the decision-making processes behind AI-generated animations can be challenging. This lack of explainability and interpretability can hinder the ability to refine, critique, or learn from the generated content, presenting a barrier to the iterative development and improvement of AI-generated animations in a business context.

Potential Solutions for Animation

Although the focus of this is primarily on the limitations of AI in animation, it’s worth briefly mentioning some promising solutions that may help address these challenges:

  1. Increase in processing power: Advancements in processors and GPUs will enable the generation of higher-quality animations with AI.

  2. Developing algorithms to ensure fairness and reduce bias: Researchers are working on algorithms promoting diverse and unbiased representation in AI-generated content.

  3. Extensive testing: More rigorous testing and refinement of AI models can help identify potential issues and ensure safe and effective use in practice.

  4. Cloud Computing: Apps and models that don’t have to be run locally can significantly reduce the resources the end user needs.


While AI has made significant strides, its application in animation still faces numerous challenges. By critically examining these limitations and exploring potential solutions, businesses can make informed decisions regarding using AI-generated animated videos in their marketing strategies. Ultimately, understanding the current limitations of AI in animation can help companies optimize their investment in video content to engage audiences better and convey their brand messages.

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