AI-Generated Content

The use of AI actors in web content is still relatively new and evolving. While AI is being increasingly integrated into various aspects of content creation, the percentage of acting performed by AI actors remains quite low compared to human actors. But AI is being used in every area of filmmaking, and it is having dramatic impact on reducing time and costs. Here are some insights:

Current Usage: AI actors are primarily used in specific scenarios, such as virtual influencers, digital avatars, and certain types of animated content. The actors, or avatars, don't have the ability to walk or move normally, and because the advances in movement are happening relatively slowly, they have not yet replaced human actors in mainstream web content. It is predicted that as soon as avitar movement is improved, AI actors could replace as many as 10% of human actors overnight. Because AI will reduce the cost, and accellerate the speed of production significantly, we might see replacement increase; and 50% of humans actors could be replaced in ten years. 

Technological Limitations: AI-generated performances often lack the emotional depth and nuance that human actors bring to their roles.

Industry Adoption: While there is growing interest in AI for content creation, the entertainment industry still heavily relies on human talent for acting. 

Overall, AI actors are a small but growing part of the web content landscape. Human actors continue to dominate the majority of acting roles online.  That being said, AI-generated content for advertisement, education and entertainment -- is exploding! What's more, the technology is advancing to quickly, resistence to adoption could be overwhelmed due to the financial advantages of free versus extremely expensive. 

Adoption may be somewhat slow, but the problems related to AI-actors lacking emotional depth and nuance - will be solved.  AI has the power to revolutionize the film production process. Today, AI generative technology has effectively -- provided every person with an Internet connection - access to the most advanced camera, a powerful telescope, microscope - all in one. Generative AI is also a powerful post-production editor, researcher, fact-checker, script analyst and editor.  AI visuals are like having any lighting effect possible. AI is capable of generating a video from any angle, distance or position - with precision, nuance, and control of every element, every factor called for in the script. The time and expense advantages are mind-boggling and almost incalculable.  The advantages in productivity are downright outrageous, especially with high-budget productions.

Until the writer of this text began to experiment with and use AI-generated content, I was focused mostly on the possible disruption and potential unemployment that could result from AI-generated content. AI-permitted me to almost effortessly produce thousands of high-quality images and videos - that would have costed tens of thousands of dollars and taken months to generate using traditional photo and video editing software.

The experience was transforming.  I now realize that the advantages of using AI-generated content are nothing short of gargantuan in scale; because the barriers to every aspect of filmmaking have stiffled production since the inception of the art.  AI it could actually liberate and expand the film industry, by actually creating the things that encourage the finance of projects - AI has the power to free capital investment - something that is probably the biggest challenge to film production and industry growth.

Imagine being able to produce a film - without having to purchase a camera and other equipment, hire directors and actors. Imagine being able to apply any type of lighting or camera = fro a dolly to a drone - without actually having to purchase this equipment.  What if you could assign specific facial expressions on all actors, assign specific voices to them, and switch the same any time? What if you could go beyond story-boarding a film project, and actually show actors performing the scenes - in the sets that are called for in the script? What if you could product an equipment list, and budget estimates for a film - automatically?  How about - engineer product placement - before shooting the film?  What if you could go on location - without leaving home? What about product placement - throughout the film? Artificially generate voices and dialog Artificially generate  images and sound? Score the film automatically? Edit the film faster? What if the AI draft permits feedback from real audiences - who would never read a script? What if revenue could be generated by limited release in isolated markets?  And perhaps the most potentially volitile question possible:

What happens when an AI-film gains widespread financial success and critical aclaim?

Cinematic AI - technical capabilities

How to Use Open-AI in Your Application

Using OpenAI’s API

OpenAI’s API offers a range of models, and while their primary focus has been on text generation with models like GPT, they do have models for image generation through different integrations. For instance:

• DALL·E: OpenAI’s DALL·E is a model specifically designed to generate images from textual descriptions. You can access DALL·E via OpenAI’s API, allowing you to generate images based on text prompts. To use DALL·E, you would need an API key from OpenAI, and you can integrate this into your application or server.

• API Access: To use the DALL·E API or any other image-generation API, you would send HTTP requests with your text prompts and receive images in response. You can find more information on how to use these APIs in the OpenAI API documentation.

2. Third-Party Services

• Hugging Face: Platforms like Hugging Face host a variety of text-to-image models. You can use their services to generate images from text prompts by leveraging their hosted models or deploying your own instances.

• Replicate: Replicate is another service that hosts machine learning models, including those for text-to-image generation. You can deploy models like Stable Diffusion or DALL·E on Replicate and interact with them via API.

3. Open Source Models

• Stable Diffusion: This is an open-source text-to-image model that you can deploy on your own server. It requires substantial computational resources, but it provides a lot of flexibility for generating high-quality images from text prompts. You would need to set up the model, handle the data and requests, and ensure your server has the necessary hardware (like GPUs) for efficient operation.

• VQ-VAE-2: This is another open-source model that generates images from text. It is available for download and use, but like Stable Diffusion, it requires a capable server setup.

4. Integration Steps

If you plan to integrate text-to-image generation into your server, the general steps are:

1. Choose a Model: Decide whether you will use a hosted service or deploy an open-source model.

2. Obtain API Access: If using a hosted service, get API access and credentials. For open-source models, download and set up the model on your server.

3. Implement Code: Write code to interact with the model. For APIs, this involves sending HTTP requests with text prompts. For local models, this involves setting up inference pipelines.

4. Test and Optimize: Ensure that the integration works as expected and optimize performance based on your server’s capabilities and requirements.

By using these approaches, you can generate images from text prompts on your server, leveraging the power of advanced AI models. Mediagration consultants are familiar with many of the cutting-edge tools for production and management of AI-generated film and other AI-powered video projects. Contact us about setting up a consultation package.