How To Create A Generative Video Model

Before discussing a generative video model, let’s talk about generative artificial intelligence.

Look at this stunning picture below –?

I know, the moment you cast a look at the picture, you’d feel marveling at its raving beauty.?

But here’s the catch.?

IT WAS NOT CREATED BY A HUMAN ARTIST!?

Yes, the image is an AI artwork called Théatre d'Opéra Spatial created by Jason M. Allen using Midjourney – a generative AI platform.??

The most interesting fact is – the painting won him the first prize by beating out 18 pieces of art, at the 2022 Colorado State Fair's annual fine art competition for “digital arts/digitally-manipulated photography”.?

It was one of the first AI-generated pictures that won the prize.?

While the image won Allen the global limelight instantly, it also brought along with it the universal derision and vehement backlash of the artists concerned with the absolute magnificence of an image that is just an AI-generated artwork.?

No doubt they are calling it a high-tech form of plagiarism.?

According to Allen, he just gave some text inputs to the Midjourney and what the AI-generative tool popped out was a breathtaking sight to behold.?

In his own words, “I couldn’t believe what I was seeing. It felt like some outward force was involved.”

This is just a very miniature scale of speechless magnificence that a generative AI tool can do in the world of art.?

And surely, in the context of Allen, this is just the beginning!

What Is Generative AI?

To put it simply, generative artificial intelligence or generative AI generates content, like text, images, audio, synthetic data, or other media based on the prompts given to it.

It is one of the most popular buzzwords in 2023 due to its features like the simplicity of new user interfaces. Users can use it to create high-quality texts, graphics, and videos in a few seconds.

Popular forms of generative AI are – ChatGPT (text-generating AI), Midjourney (image-generating AI), DALL-E 2, and Bing AI.?

What Is The Objective Of Generative AI?

Generative AI tools are dominating the content creation industry. From ChatGPT to Midjourney, these AI tools are transforming businesses.

For example, while Microsoft partnered with OpenAI, Google is involved in building its brainchild AI-powered chatbot called Bard. The indication is clear. Generative AI is growing fast into becoming one of the hottest areas within the tech sphere.

The objective of generative AI is to produce new data replicating the training dataset used to train the machine learning model.

The training dataset, which forms a pivotal part of the machine learning model used by generative algorithms, represents a substantial subset of the original data. This training data is essential for these generative models to acquire the necessary knowledge of patterns and predictive capabilities required for their designated tasks in AI software development .

Since there are different types of generative models that can produce content or simulations, the one we are discussing is all about generative video models.

What Are Generative Video Models?

Generative video models are machine learning algorithms. Therefore, the models generate video content according to the pattern that it reads and relationships learned from training datasets.

Video data is synthetically created similar to the original ones based on how the models allow learning the underlying structure of video data.

Training approach within different generative video models is based on their respective unique infrastructure.

When it comes to creating video data, these models depend on the prompts in terms of a user’s requirements through the text, etc. The models generate videos based on the textual instructions. Based on your tools, the models can also make use of image prompts to create videos.

Generative video models make available a plethora of opportunities for video content creators.

From producing videos using text prompts to generating new scenes and characters, along with improving the quality of the video, the potentiality of generative video models is endless.?

Cutting-edge models like GANs, VAEs, or CGANs that power generative video platforms come equipped with the capability of building lifelike images and videos based on inputs.?

A Brief Rundown On Generative Models, And Their Types

As outlined before, data created through generative models replicate the training dataset using ML algorithms. According to the technology research firm Gartner, generative AI will be creating 10% of all data, with 20% of all test data for consumer-facing use cases by 2025.

Also, generative AI will help 50% of development initiatives and drug discovery by 2025. For the manufacturing industry, the use of generative AI would revolve around 30% to empower the effectiveness of their product development by 2027.

The thing with generative models is that they undergo a series of training to be able to generate content based on the prompts (be an image or text prompts).

?An Explanatory Note To Dall-E, ChatGPT, And Bard

Dall-E – It is a deep-learning model to generate digital images based on prompts (like natural language descriptions). The model is an example of a multimodal AI application that interprets words into visual elements. Developed by OpenAI, it can generate imagery in numerous styles based on user prompts.

In the following picture, Dall-E generated an image with the prompt “Teddy bears working on new AI research underwater with 1990s technology.”

ChatGPT – It is a chatbot powered by artificial intelligence and developed by OpenAI. Its prototype version, launched in 2022, drew the limelight for its ability to finetune text responses through a chat interface.

A member of the GPT (generative pre-trained transformer) family of language models, the tool simulates real conversation. It drew attention for its detailed response and articulate answers across various domains of knowledge.

Bard – An experimental chatbot of Google, Bard is based on a language model for dialogue applications or LaMDA. It was launched as Google’s rushed response to OpenAI's ChatGPT and Microsoft's Bing Chat.

The aim behind its launch was to make it a key part of the Google search experience, as it is designed to help users brainstorm and answer queries.?

Types Of Generative Models

Generative Adversarial Networks (GANs)

The model is based on two parts – GENERATOR which creates plausible/fake data, and DISCRIMINATOR which differentiates fake data from real data. Basically, the discriminator model checks the authenticity of plausible data.

The objective of the generator is to generate fake data, whereas the goal of the discriminator is to check the authenticity of fake data.

However, there is an interesting fact here.

When generator training continues to a point that it becomes so convincing that the discriminator fails to tell the difference between fake and real. This is the stage wherein its accuracy dwindles.?

Flow-Based Generative Model??

Also called the stable diffusion model, the flow-based generative model is an ML-based generative model. The use of this model involves translating a simple random noise into more structured and complex data, such as a video or an image.

For this, the model defines flows or a series of simple transformations to slowly transform the random noise into the expected data. The model is also constructed by a sequence of invertible transformations.?

Autoregressive Models

In this model, data is generated one piece at a time. It means generating a word in a sentence at a time. How is that made possible? Well, the model predicts the next piece of data, according to the previous pieces.

Variational Autoencoders (VAEs)

In this model, training data is encoded into a latent code. It is basically a lower-dimensional representation of the encoded training data. After that, the latent code is decoded into the original data space.

This is where new data is generated. The purpose is to identify the most valid latent code to initiate generating data as a replica of the original one.?

Convolutional Generative Adversarial Networks (CGANs)

It is a type of deep learning network to generate data similar to the input training data. A type of Generative Adversarial Networks for image and video data, CGANs use convolutional neural networks.

It ensures learning the relationship between diverse parts of an image or a video. This makes them well-suited for tasks like video synthesis.?

Remember, generative models are many and each has its respective purpose based on the specific rudiments of the task.?

What Generative Video Models Can Do?

Video Compression??

It involves encoding the original video into a lower-dimensional representation and decoding it to create a synthetic video. As a result, it allows the model to compress the video file without affecting its quality.

Video Synthesis??

The model is used to produce a new video frame to complete a partially completed sequence. It comes in handy for creating new video footage from still photographs. You can also use it to replace the missing frames in a damaged video.

Video Prediction??

Generative video models can prove efficient in predicting the next frames in a video by interpreting the currently playing video. For this, the model requires patterns and relationships discovered from the training data. Video prediction tasks involve security monitoring, autonomous driving, etc.?

Video Style Transfer??

Video generative models can execute the task of transferring one video style to another. It results in creating distinctive and innovative visual effects. There is also a chance for making the video look more distinctive by applying a style of famous artwork.?

Understanding The Working Mechanism Of Generative Video Models

The typical tendency of any AI model is that it needs to be trained on a large dataset to perform decision-making processes without human interference.

Similarly, generative video models require training processes depending on a model’s architecture, to be able to generate a new video.

In this context, we are discussing two distinct models called Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).

Generative Adversarial Networks (GANs)??

They are exciting innovations as a machine learning framework or generative models for creating new data instances conforming to the training data.

To say otherwise, a trained GAN about photographs can help you create superficially authentic new photographs, with many realistic characteristics of a real human.

Apart from being a generative model for unsupervised learning, it is also useful for supervised learning. The model features two core components called a generator and a discriminator, both are neural networks.

The generator model creates fake videos, whereas the discriminator evaluates the authenticity of the generator’s videos and provides feedback to it.?

The generator in GAN creates a video using an input called a random noise vector.

The “fake” video then is presented to the discriminator as an input, which then produces the result of whether the video is “authentic” based on its probability score, usually between 0 and 1.

Interestingly, both generators and discriminators are trained differently.

For instance, generators are trained to create a fake video that a discriminator can’t detect, whereas the discriminator is trained to validate the authenticity of the video created by the generator.

The cycle of evaluating the video data continues until the discriminator is no longer able to distinguish between fake and real video.

Variational Autoencoders (VAEs)?

A generative model of VAE consists of two key components known as an encoder and a decoder, respectively. As a neural network, the encoder outputs a lower-dimensional representation of a video that it maps. It is known as a latent code.

The video that an encoder maps into a latent code becomes a parameter to parameterize a probability distribution.?

On the other hand, the reversal of the process is executed by the decoder. A term known as a reconstruction loss, which stands for training an undercomplete autoencoder, is calculated in a VAE using the decoder.

Here, the decoder maps the latent code back to a video and subsequently compresses it to the original one. Note that a well-trained VAE can be leveraged to create a new video in which the latent codes are sampled from an initial distribution and passed them through the decoder.

How To Create A Generative Video Model

Create An Ideal Environment??

This is the first step to creating a generative video model. You need to choose the right programing language to write codes.

Once you have chosen the programing language, install software packages, including a deep learning framework. You will also require additional libraries to preprocess and visualize your data. Note that deep learning framework includes TensorFlow, Keras, or PyTorch.?

Design The Architecture Of The Model??

A generative video model requires an architecture for determining the quality and robustness of the generated video sequences comprising numerous frames linked by time. It requires caution, considering sequential video data is extremely important when you design the architecture of the generative model.

Things to be followed to design the architecture of the model include; determining the input and output data format, choosing StyleGAN if you consider base architecture, and adding the encoder-generator networks.

Evaluate And Finetune The Model??

In model evaluation, different evaluation metrics are involved to understand the performance of a machine learning model, its strength, and its weakness. So, model evaluation basically determines the quality, efficacy, and efficiency of the model by identifying improvement areas and finetuning its parameters to make the model’s functionality better.

The process of model evaluation involves quantitative metrics to evaluate and visually examine the quality of the generated video sequences.?

The Quantitative Metrics:

• The structural similarity index measure (SSIM) - It is one of the quantitative metrics used for measuring the similarity or resemblance between two images, as well as predicting the perceived quality of digital television and cinematic pictures.
• The Mean Squared Error (MSE) – It is another quantitative metric or an estimator used to estimate a given quantity based on observed data.
• Peak signal-to-noise ratio (PSNR) – It is used to quantify reconstruction quality for images and video (subject to irreversible compression).

Once the evaluation process is over, finetune the model based on the results of the evaluation. It requires adjusting the architecture, training process, or configuration to optimize its performance.

A term in machine learning known as a hyperparameter also needs to be optimized by following certain procedures, including tweaking the model’s parameters.?

Build A Web User Interface (UI)

The importance of integrating video content into web pages and mobile screens can’t be downplayed. In fact, it is a trending user experience design model.

In our context of creating a generative video model, building a web User Interface (UI) is unavoidable to ensure that the end-users interact with the model, enabling them to feed necessary input parameters, including –

●??????Style types

●??????Effects

●??????Image rescale

●??????Style degree

To make it happen successfully, design typography, colors, layout, and other visual elements according to your predefined parameters. Based on the design model, develop the front end.

After building the UI, make sure it is tested meticulously to make it bug-free and optimally functional. There are many machine learning models with friendly web interfaces you can use to develop custom UI .

Deploy The Model

After training the model, finetuning, and building the web UI, the last phase of creating a generative video model is deployment to a production environment. The deployment will require certain things, such as setting up a data processing and streamlining pipeline, and making configurations to the software and hardware infrastructure.?

Conclusion

Creating a generative video model involves delicate and complex procedures. It is a painstaking task of preprocessing the video dataset. You have to design the model architecture and add layers to the common architecture, along with handling training and model evaluation.

An approach to generative modeling called Generative Adversarial Networks (GANs) uses deep learning methods and is frequently used as the foundation architecture in creating a generative video model.

Another foundation architecture for similar use is Variational Autoencoders (VAEs), an autoencoder for designing complex generative models of data and fitting them to large datasets.

Applications for creating generative video models are many, including video synthesis, and video toonification. If trained well, image-oriented models can be used to process huge volumes of data, identify patterns, and detect anomalies. In the context of generative video models, the trained image-oriented models can help you produce high-quality videos with adaptable style settings.

Lastly, generative video models are the fastest-evolving domains parallel to emerging cutting-edge technologies and machine learning app development to improve the quality of generated videos.