ad
ad

How AI generated videos work

People & Blogs


Introduction

Do you notice anything unusual about these videos? Well, there's technically nothing wrong with them except for one interesting fact: these videos are generated by artificial intelligence from a single image. Let's delve into how this model operates, which is quite fascinating.

These AI models are trained to grasp the structure of an image by manipulating noise levels—both adding and removing them. For instance, if you present the model with a picture of a dog, you would progressively add noise. This process helps the AI understand the image of the dog as if it were seeing it with its "eyes closed."

Now, when we provide the model with an image of just noise, it can reverse this process. The AI model uses its learned knowledge to reconstruct and modify the image. It can then apply these changes and create variations. So, if you instruct it to generate a picture of a dog with laser beam eyes, wearing a top hat, and floating in space, the model will give you something like this:

AI Generated Dog

Surprisingly, it's not too bad, right?

Keywords

  • Artificial intelligence
  • AI-generated videos
  • Image noise
  • Image processing
  • Model training
  • Video generation

FAQ

Q: What is unique about these AI-generated videos? A: These videos are created from a single image using artificial intelligence, which is trained to add and remove noise to understand the image structure.

Q: How does the AI model learn to generate videos? A: The AI model learns by progressively adding noise to an image, enabling it to understand the structure. This process is later reversed to create new variations from noise.

Q: Can the AI model generate any kind of image variation? A: Yes, after training, the model can follow complex instructions to create various images. For example, it can generate a dog with laser beam eyes wearing a top hat in space.

Q: What is the underlying mechanics of this AI model? A: The model is trained to understand the structure of an image by adding noise to it, and then reversing the noise addition process to generate variations from an image filled with noise.