It’s no secret that AI could completely change filmmaking and how the entertainment industry operates. Right now, many studios are doubtful about it, and the topic is still contentious. Still, some studios are cautiously experimenting with it. As the world adjusts to this breakthrough technology, we are bound to see its use across the media and entertainment pick up gradually. Let’s look at the current climate around AI in filmmaking and media and discuss some of the possibilities it offers. Then, we will look into the technical challenges of implementing it, including the need to assess your data management infrastructure.
The current discussion around using AI in filmmaking and other forms of content
The use of AI in film and media is obviously still a topic of debate, just like other industries. Let’s look at some of the ethical and legal considerations of using this technology, then go over some of the potential benefits.
Ethical considerations
The media and entertainment industry is still getting over the history-making strikes in Hollywood last year. While agreements between the Writers and Actors Guilds and the Alliance of Motion Picture and Television Producers (AMPTP) included protections against AI, there are many other roles across the industry that could be impacted by its adoption.
Legal considerations
Whether or not studios even have the right to use AI-generated art in their projects is still a bit murky, legally speaking. As an example, in a copyright lawsuit filed by visual artists last year against Stability AI, Midjourney, and other AI image generation companies, the court dismissed many of the artists’ claims (including copyright infringement), though a claim for direct infringement against Stability AI was allowed to proceed.
The benefits of using AI in filmmaking
It is still hard to believe that a world could exist where people are happy to watch a movie they know is totally or even mostly generated by AI. We’ll always need human beings to moderate its input and output. For one, there’s evidence that AI’s output breaks down if it is continually fed AI-generated content. Original, unique input from people is necessary to prevent it from turning out to be nonsense.
Examples of how filmmakers use AI in production
1. Creation of preliminary storyboards
Gen AI can help give a rough shape to a character, object, or scene, or even generate a rough storyboard. These can serve as a starting point so an artist doesn’t have to create something completely from scratch.
2. Recreating real spaces and objects
Manually creating a digital replica of a real-world space is painstaking. With neural radiant fields (NeRFs), you can recreate 3D environments with stunning photorealism from 2D images.
3. Generating realistic background characters
Designing hundreds or thousands of unique background characters with unique faces would be incredibly time-consuming. AI can automatically generate countless characters to populate your scenes.
4. Aging and de-aging
AI has already saved actors like Tyler Perry and Tom Hanks hundreds of hours in the makeup chair. Gen AI has been used in films (like the upcoming film “Here”) to age or de-age them in post-production.
5. Post-production
AI streamlines the editing and post-production processes by automating tasks such as color grading, sound mixing, and removing objects from scenes. AI-powered editing software can analyze footage and make recommendations.
All of us remember the infamous Starbucks to-go cup in Game of Thrones. If they had used AI in editing, it would have caught the issue before they aired the episode.
6. File management
AI helps rename files, automatically apply textures to thousands of files, auto-tag files for quick bulk uploads, apply descriptive metadata, and create more accurate transcripts and captions. AI can also make it faster and easier to find existing assets in a digital asset manager.
Challenges of using AI in filmmaking
1. Quality control
Humans need to closely review and revise everything generative AI creates. People want the art they see to be made by other people, not computers.
2. Data management
Data management remains a challenge. AI will only introduce exponentially more data into the picture, and teams will need to have scalable and powerful infrastructure in place in order to manage it.
3. Storage and processing limitations
It takes a tremendously large dataset to train AI, so studios who opt to train AI on their own intellectual property will need storage for millions of files, as well as very powerful graphics (GPU) intensive server hardware.
What does the future look like?
AI is set to permanently change the way the media & entertainment industry creates art. Some studios are bound to feel pressure to adopt it sooner rather than later, for fear of falling behind the competition and being overlooked in favor of studios that can create better work, faster, thanks to AI. If you’re developing art for movies, television, or other media using modern technology like game engines, then you need a version control system. Thus, a future without AI in filmmaking seems hard to believe. The extent and scope, however, still remain undecided.
