An AI system that Generates Videos from Text

Generative AI is the new buzzword for 2023. Generative AI tools, whether text-generating ChatGPT or image-generating Midjourney, have revolutionised businesses and dominated content creation. Moreover, it is rapidly growing to be one of the most popular areas in the tech world thanks to Microsoft's partnership with OpenAI and Google's AI-powered chatbot Bard.

Generative AIis a method that generates new data that is similar to the original training dataset. To learn the patterns and distributions in the training data, it uses machine learning algorithms called "generative models." Many generative models can produce text, images, and audio codes. This article will focus on generative video models.

Generative AI has been a hot topic and continues to grow in popularity. Gartner included generative AI as one of the most important and rapidly changing technologies to bring about a productivity revolution in its Emerging Technologies and Trends Impact Radar 2022 report.

What is Generative AI? Why should you care?

Generative AI is a combination of semi-supervised and unsupervised machine learning algorithms. These algorithms allow computers to create new content using existing content, such as text, audio, video, images, and code. The goal is to create original artefacts that look exactly like the real things.

Generative AI allows computers to abstract the underlying patterns of input data to create or output new content.

At the moment, there are only two generative AI models that are most popular, and we will examine both.

Generative adversarial networks (GANs) are technologies that create visual and multimedia artefacts using textual input data and imagery.

Transformer-based models -- technology such as Generative Pre-Trained language models (GPT), which can use information from the Internet to create textual material, including press releases and whitepapers.

In the intro, we shared some cool insights highlighting the bright future for generative AI. Because this technology can mimic any data distribution, the potential for generative AI and GANs is enormous. This means it can be used to create worlds very similar to ours in any domain.

In transportation, which heavily relies on location services, Generative AI may be used for converting satellite images into map views to enable the exploration of previously unexplored locations.

The travel industry uses generative AI to help with face identification and verification. It creates a complete picture of the passenger using photos taken at different angles.

With the help of GANs, sketches-to-photo translation can convert X-rays and CT scans into photorealistic images. This allows for the early diagnosis of dangerous diseases such as cancer.

Although it might look this way, generative AI is not magic. It needs to be modelled to create artefacts out of real-world content. Here's how.

What is Generative Modeling?

Generic algorithms are the exact opposite. Instead of predicting features based on a given label, they attempt to predict them. While discriminative algorithms focus on the relationships between x, y, and z, generative models are concerned with how to get x.

Mathematically, we can use generative modelling to calculate the likelihood of x and/or y happening together. It learns the distribution of individual features and classes, not the boundary.

Referring to the example above, generative models can help answer the question, "What is the cat itself?" The viz shows how a generative model can predict the tail and ear features for both species and other features within a class. In addition, it learns the relationships between features to show how these animals look.

If the model can identify which types of cats and guinea pigs are common, their differences will also be known. These algorithms can recreate images of cats or guinea pigs even if they are not part of the training set.

A generative algorithm is a method that models a process holistically without discarding any data. It is possible to wonder, "Why are we using discriminative algorithms?" Often, a more specific algorithm solves the problem more effectively than a more general one.

However, generative modelling can still be used to solve many problems. GANs and other transformer-based algorithms are two examples of such innovative technologies.

How does Generative Video Modelling Work?

Like all AI models, generative video models are trained using large data sets to create new videos. The training process can vary from one model to the next, depending on its architecture. Let's take the example of VAE and GAN to see how it might work.

Variational autoencoders (VAEs)

Variational autoencoders (VAEs) are generative models that generate videos and images. The two main components of a VAE are an encoder and a decoder. A decoder reverses the process. For example, an encoder converts a video to a lower-dimensional representation called a latent code.

To model the distribution of videos in training datasets, a VAE uses decoders and encoders. First, each video is converted into a latent COD, which can be used to parametrize a probability distribution, such as a normal distribution. Next, the decoder maps the latent codes to videos and compares them to calculate the reconstruction loss.

The VAE encourages latent codes from prior distributions to be followed to maximize the variety of the generated videos. This minimises reconstruction loss. Once the VAE is trained, it can generate new videos by sampling the latent codes from a previous distribution and passing them through a decoder.

Generative adversarial networks (GANs)

GANs are deep-learning models that can generate images and videos from a text prompt. GANs have two main components: a generator and a discriminator. The generator and discriminator are neural networks that process video input to produce different output types. The generator creates fake videos. However, the discriminator evaluates the originality of these videos to give feedback to the generator.

The GAN generator creates a video using a random noise vector. The GAN generates a video by using videos as input. Discriminators then calculate probability scores that indicate the video's likelihood of being genuine. If the training data is used, the generator classifies these videos as real and stamps the generated video as fake.

Wrapping Up

It is a complex process to create a generative model of a video. These steps include pre-processing the data and designing the model architecture. Layers can be added to the base architecture by training the model and then evaluating it. Generative Adversarial Networks or Variational Automat coders are often used to build the foundation architecture. Adding convolutional, pooling, or recurrent layers can increase the model's complexity and capacity.

Video style transfer, video synthesis, and video sonification are just a few of the many uses for generative models. In addition, image-oriented models can be used to create artistic videos of high quality with adaptable style settings. New techniques and models in generative video Modeling, like text-generating ChatGPT, are constantly being developed to enhance the quality and flexibility of the created videos.