What is the Future of Video Editing and Production with AI?

The race to create workable generative artificial intelligence (GenAI) apps began in earnest around 2016 when OpenAI (the creators of ChatGPT) was first founded.

2020 saw a marked acceleration in GenAI development and large language models (LLMs.) LLMs are advanced computer programs that understand and generate human-like text based on the patterns and information they’ve learned from vast amounts of written data.

ChatGPT is an LLM. OpenAI’s founders have fed ChatGPT data from sources all across the internet, which is why it can produce human-like responses.

The release of ChatGPT in 2022 was a crucial turning point because it led to widespread public awareness. Every business and individual sought to understand how AI could help them become more profitable. 

AI then moved from text to graphic generation to video editing and production. In the early 2020s, some apps allowed users to create videos by simply describing what they wanted.

Our purpose in this blog is to examine what role AI will play in videos by dissecting the following points:

• A brief history of AI and video production.
• How AI supports different elements of video production.
• What the future of AI holds for video production.

A brief history of AI and video production

AI first became involved in video in the 2010s, performing simple but effective tasks like sorting video footage and identifying key moments. This saved film editors heaps of manual labor time and allowed them to release the finished product quicker than previously possible.

via GIPHY

Editors before AI.

In 2012, there was a breakthrough in deep learning and neural networks. Deep learning is a type of AI that uses neural networks to make decisions from large amounts of data, mimicking the way the human brain processes information.

In the video production context, the breakthrough opened up several exciting possibilities:

• Automated video editing.
• Enhancing visual effects and overall video quality.
• Real-time video analysis.
• Content personalization and recommendation.
• Automated captioning and transcription.
• Deep Fake videos.
• Efficient video compression.

Overall, deep learning facilitated further automation of the video production workflow, allowing editors to spend more time on the creative and fun parts of video making.

Like all other forms of AI, video AI took off in the early 2020s. The AI-powered video editing software market is projected to reach $1,032 million by 2032.

(Image source.)

How AI supports different elements of video production

As we’ve alluded to in the above list, AI is now intelligent and powerful enough to support several aspects of video production. We’ll split these into:

• Pre-production
• Production
• Post-production

Pre-production

In pre-production, text-based AI generators can assist with fleshing out scripts, shot lists, and character personas from concept ideas and analyzing a script’s effectiveness in reaching the intended audience. 

This speeds up and beats any writer’s block present at the beginning of the video production process. Oftentimes, someone has an idea in their mind but struggles to pen it onto paper. Here’s where AI text tools become an important writing assistant. 

As long as the script details the scene, it can be fed into AI visual and graphic generators to produce a storyboard.

Production

AI “wow!” moments truly begin in the production stage onwards. 

AI-powered cameras have become a thing. They can:

• Autonomously track subjects and adjust framing, eliminating shaky footage and allowing for smooth and professional shots without a human camera operator.
• Analyze a scene and suggest optimal color grading, camera angles, lighting, and composition. 
• Improve a video in real-time by reducing background noise, image and video stabilization, and low-light performance enhancement.
• Analyze environmental conditions and adjust camera settings appropriately in real-time.

As experienced video creators will have noticed, AI cameras actually perform a lot of the legwork required in the post-production process during the shoot, saving overall go-live time.

Post-production

With 20% of post-production tasks done at production, the remaining 80% are a breeze. AI assists the post-production process in several ways.

The first is automated video editing. Because AI has effectively “learned” video editing from the vast amount of data it’s been fed, AI tools can almost edit like a human by:

• Identifying the best shots and scenes relevant to the narrative.
• Deleting duplicate shots.
• Creating rough cuts.

AI video editing tools don’t eliminate the need for a human editor. Still, AI gives them a significant head start by sifting through the raw footage, only giving the human editor high-quality footage to further splice and dice.  

What we once thought AI might look like–and still could? (Image source.)

Furthermore, AI can automate sound editing tasks, sync sound effects with visual elements, and adjust audio levels for clarity.

Visual and special effects are another traditionally time-consuming area partially automated by AI. By feeding AI-specific prompts, it can create animations, produce creative color grades, simulate natural phenomena and remove unwanted objects from the footage.

Budget films often don’t have the sums to invest in all the bells and whistles. Here’s what AI can do for you:

• Compose background music to complement the narrative, reducing the reliance on stock audio that creators often find themselves compromising on.
• Add voice-over and dubbing without needing to hire a voice actor.

And finally, AI makes asset management less despairing by automatically tagging video assets, making it easier to find specific clips or footage.

By examining the pre to post-production journey, AI’s benefits in video production become clear. Automated editing and video/sound engineering save time, while AI voice generators reduce the investments required in human labor–with all benefits realized without sacrificing video quality–rather, the quality improves.

The future of AI and video production

AI’s future role in video production is promising and exciting. Although everyone will have their interpretation of where AI and video are headed, they can be broken down into four categories.

More of the same

There’s no reason to suggest that AI algorithms won’t keep getting smarter, as they have in the past.

Machine learning (ML) is the name given to a type of AI that enables computers to learn from data and improve their performance on tasks over time without being explicitly programmed.

As MLs evolve, so will the quality of AI outputs. This means that the aforementioned pre-production, production, and post-production advantages will compound, allowing creators to derive greater benefits.

The role of natural language processing (NLP)

NLP will make video production and editing accessible to non-technical users. So, if you have a vision, AI and NLP can bring it to life without requiring any technical video production knowledge. 

NLP refers to AI’s ability to understand and respond to human-like language. In practical terms, this could mean:

• Creators explain edits to AI software using spoken words, and the AI tool understands the requirements and turns around each request accurately.
• Automatic subtitle generation. This isn’t simply recognizing and creating text from speech. AI can understand the context of the words it needs to transcribe. Even using an AI translator can be particularly helpful when translating to different languages and dialects that have their nuances.
• Providing contextual scene summaries and recommending context-appropriate scripts. Essentially, AI will be able to mimic human conversations and retain the memory of each interaction, ensuring a conversation develops like it would in the real world. This helps derive accurate and culturally appropriate scripts and overcome any writer’s block in script writing.

Real-time collaboration

AI-enabled cloud platforms will permit teams of editors (no matter where they are in the world) to collaborate on a live video project simultaneously. 

Such collaborations enable:

• Users can see each other’s cursors, selections, and the scenes they’re working on.
• Real-time commenting and feedback.
• Timestamps to see how the edits have progressed.

Videos are produced quickly without being constrained to traditional sequential editing. Teams can share and test ideas in real-time, so the whole editing process becomes heavily streamlined.

Do we need human video producers?

The most exciting development in the AI video space is the ability to create fresh footage from text-based prompts. So, someone could visit an AI video generator like Steve.AI and describe a scene in written words, and it’ll produce a high-quality and relevant video.

As it stands, AI video generation tools solve the stock footage problem. Creators on a budget are stuck with choosing awkward and overly staged stock footage whenever they need to add cutaways or explain an idea.

With AI video creators, there’s no need to tolerate stock footage that doesn’t fully encapsulate a creator’s vision. 

Will such apps replace human video producers? No.

No matter how advanced AI becomes, conducting the orchestra will always require a human.

Wrapping up

Video production and editing have entered an exciting phase, almost tantamount to when we discovered videos with sound (the late 1920s), videos with color (1930s), electronic video editing (1970s), digital videos (1980s), and various resolution advances.

However, the current phase seems particularly game-changing because it democratizes video editing. Not only can anyone make a high-production value video, they can do so quickly without prior technical knowledge.If you want to dabble in videos, why not experiment with Steve.AI’s AI video tool and see the power of AI videos first-hand?