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Creative AI’s generate video from plain text

Imagine not being able to program or code, but still being able to write a description or a script and have an AI create an HD image or video of it for you, this is the technology that's now arriving.

Key takeaways

  • The AI then studied these clips and learnt to identify each motion, refining its neural network and refining itself all the time.
  • With a dataset in place, the researchers then used a two step process to create the generative video.
Cite or link to this article

Griffin, M. (2018) 'Creative AI’s generate video from plain text', 311 Institute, 4 April. Available at: https://www.311institute.com/creative-ai-use-plain-text-to-create-videos-out-of-thin-air/ (Accessed: 8 October 2026).

In 2016 an Artificial Intelligence (AI) won an award for best short film at the Cannes Film Festival in France, in 2017 another created the world’s first AI music album for Sony, and elsewhere others began innovating and creating everything from winter scenes to help create better self-driving cars, to new product designs, including clothing, sneakers and even the world’s first self-evolving robot. And all these AI’s have one thing in common - they’re all “creative.”

AI is getting better and better at creating what’s known as “Generative content,” in short, content, such as images, music and scripts, or, let’s face it, text, that AI’s are able to make by themselves with little or, as is more the case, no input from humans, and recent examples include photo-realistic images of fake celebrities and an increasing number of new, other, AI composed music albums from artists such as Amper, DeepBach, Magenta, and Flow Machines, all AI's. Now though scientists are working on building AI’s that can create generative video. The idea is that simply by typing out a phrase AI could create a video of that scene, and scientists at Duke University and Princeton University, following on from Microsoft who recently unveiled their own version that does the same but just for images, have created a working model.

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Some examples, small today, bigger and better tomorrow

"Video generation is intimately related to video prediction," say the authors in their new paper. Video prediction, where AI attempts to predict what actions come next in a video, has long been a goal of many AI researchers, and for obvious reasons, security companies, but so far, other than a product preview from MIT whose AI managed to predict what happened next in a cycle race, there have been relatively few successes.

Visual representations, however, especially moving ones, often contain a wide variety actions and outcomes so as a first step the researchers used a narrow range of easily defined activities, which they took from Google's Kinetics Human Action Video Dataset, for their AI to learn from including sports, such as cycling, football, golf, hockey, jogging, sailing, swimming and water skiing. The AI then studied these clips and learnt to identify each motion, refining its neural network and refining itself all the time.

With a dataset in place, the researchers then used a two step process to create the generative video. The first step was to create an AI that could generate video based on just a text description, and then came the second stage, the creation of a second "Discriminator" AI.

For example, if the text input was to create a video of "biking in snow," the first AI would produce a video and the second, the discriminator would judge it and compare it to a real video of someone biking in the snow, and any improvements or recommendations would be automatically fed back into the model so that over time the results got better and better until the generative video was indistinguishable from the real thing.

While the teams work is still in its earliest stages, with the new AI only capable of creating videos that are 32 frames long and the size of a postage stamp, over time they will get longer, bigger and better quality, and as it turns out the AI is finding humans, with our bodies and our unpredictable actions, the most problems, but to get a better grasp on us flesh bags the team are now training it to understand how the human skeleton works.

Beyond the obvious nightmare of fake news generation, an example of which I showed off recently during my talk on the Future of Trust in London, where another generative AI was used to create a thoroughly convincing fake Obama news clip, there could be actual use for generative video, such as using it to help train self-driving cars better by helping produce realistic road and traffic simulations, or helping athletes train better by simulating game play.

Either way it’ll be a while before we see any AI produced films, but we’re now at the start of our journey, and if following AI developments has taught me one thing, it won’t be decades before we see one, it’ll be years.

FAQ

Why does this matter?

Imagine not being able to program or code, but still being able to write a description or a script and have an AI create an HD image or video of it for you, this is the technology that's now arriving.

Matthew Griffin

About the author

Matthew Griffin Founder, 311 Institute

Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath."

Read full bio

Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath." 15-time best selling author of the "Codex of the Future" series, Matthew is the Founder and Futurist in Chief of the 311 Institute, a global Futures and Deep Futures advisory firm working with royal households, world leaders, G7, G20, and G77 governments, NGOs, and multi-national mid and mega cap firms to help them explore, shape, and lead the next 50 years of business and society.

An award-winning YouTube creator with over a million followers, with an unrivalled global reach and impact, Matthew is a highly sought-after international keynote speaker, lecturer, and mentor who collaborates with global leaders through the United Nations Alliance of Civilizations (UNAOC) and United Nations General Assembly (UNGA) to shape pivotal initiatives such as the UN’s AI for Humanity program, the United Nations Conference of the Parties (UN COP), and the World Economic Forum in Davos.

As the former Global Head of Cloud, National Security, and Enterprise Sales for companies including Atos, Dell-EMC, and IBM, Matthew has a proven track record of building multi-billion dollar business units and turning failing divisions into market leaders. His ability to identify, analyse, and communicate the implications of hundreds of emerging technologies and trends is unparalleled, and his insights are trusted by many of the world’s most respected organisations, including ABB, Accenture, Adidas, AON, ARM, BCG, Centrica, Citi, Coca-Cola, Dentons, Deloitte, Dow Jones, EY, Google, KPMG, Lego, Legal & General, LinkedIn, Microsoft, PepsiCo, Qualcomm, RWE, Samsung, Siemens AG and Siemens Energy, T-Mobile, UBS, VISA, Walmart, Workday, Worldpay and many others.

Regularly featured in the global media including the AP, BBC, Bloomberg, CNBC, Discovery, Forbes, Khaleej Times, Telegraph, TIME, ViacomCBS, WIRED, and the WSJ, Matthews mission is to help organisations create a fair and sustainable future whose benefits are shared by everyone irrespective of their ability, background, or circumstances.

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Sources and further reading

  1. 16152 72279 GB.pdf aaai.org
  2. Kinetics Human Action Video Dataset deepmind.com

Source: first published by the 311 Institute on 4 April 2018. Cite as: Griffin, M. (2018). Creative AI’s generate video from plain text. 311 Institute. https://www.311institute.com/creative-ai-use-plain-text-to-create-videos-out-of-thin-air/

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