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A Frame-by-Frame Look at How Generative AI Supercharges Creativity

A Frame-by-Frame Look at How Generative AI Supercharges Creativity

AI AI, AI & Emerging Technology Consulting, Digital transformation, Experience 6 min read
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Labs.Monks

A landscape animated mountainside with mist and fog

By now, you’ve seen it all over social media: uncanny images painted by artificial intelligence. Fun to play with thanks to its accessibility, generative AI has exploded in popularity online. But it’s also raised questions about the nature of human creativity: what is the value of artistry and craft if anyone can generate images in a few seconds?

The impressive output of generative AI has led some to voice concerns about whether their livelihoods are in jeopardy. Creativity, after all, has long been considered a strictly human skill.

But creatives aren’t about to lose their jobs to robot overlords who can spin strings of text into pixelated gold. To the contrary, these tools—which rely on human input and some level of artistic aptitude to really shine—are unlocking creative potential and helping people bring their concepts to life in new ways. This outlook prompted the Labs.Monks, our research and development team, to explore how generative AI can uplevel the work of our teams and our clients.

“We’ve been playing with this technology for a while, and after it began to trend, we’ve been getting more and more questions about it,” says Geert Eichhorn, Innovation Director and Head of Labs. For instance: a lot can be said about the future of content creation aided by AI, but how could today’s tools integrate into a present-day production pipeline? 

Looking for an answer, the Labs.Monks collaborated with animators and illustrators on our team to develop a prototype production workflow that blends traditional animation methods with cutting-edge AI technology. The result is an animated film trailer made using a fraction of the time and resources that a typical, frame-by-frame animation of its length would require.

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Learn to live with the algorithm.

Ancestor Saga is a 2D-animated side project focused on a central question: what if people in the Viking Age realized they were living in a simulation? After learning that their purpose in life is to entertain the gods, will they accept their new reality, or put an end to the world by bringing about Ragnarök?

The theme might feel familiar to anyone trying to make sense of the increasingly algorithmic world we’ve suddenly found ourselves in. “We wanted to tell a story that could integrate with the tech we’re using: virtual worlds and virtual people,” says Samuel Snider-Held, Creative Technologist. Associate Creative Director Joan Llabata takes this thought further, citing some of the challenges faced when humans and AI don’t quite connect. “There’s some space where we need to find the best way to communicate with the machine effectively,” he says.

When using generative AI, a bespoke approach is best.

That challenge of getting humans and AI to play nice demonstrates the need for a team like the Labs.Monks to experiment with the tools that are available. While off-the-shelf tools are great for empowering individual creators, integrating them into team pipelines requires a more custom solution.

AI is designed to do specific tasks very, very well. Projects that involve multiple capabilities and phases call for a workflow that can integrate a variety of generative AI to fulfill different goals throughout. With an animation project, this means plugging into creative concepting, storyboarding, sound and of course animating the visuals.

In our case, says Snider-Held, “We wanted to explore how AI could allow us to do the work we really want to do, even if the time or the budget isn’t there.” He found that while our animation team loves classic, frame-by-frame animation, the method is often overlooked because it is slower to produce and less cost-efficient than other ways of animating. 

Now the team had a clear goal: orchestrate an AI-based workflow that could output a frame-by-frame animation in record time, without compromising quality. They took inspiration from rotoscoping, a method used by animators like Ralph Baskhi, in which an artist traces images over existing footage. This task of translating an existing recording from one style to another was ideal for image-to-image generative AI. In addition, the team used AI technology to develop background designs and read out the animation’s voiceover.

Generative AI isn’t a radical departure from tradition.

The team began by recording a 3D character model in a virtual setting, capturing a variety of poses for an illustrator to trace over. These visuals were then used to train the AI model on how to draw the character in different movements. “If you draw about five frames, you have enough to teach a neural network how to paint the others,” says Snider-Held, noting that it’s important to select frames that are different from one another so the AI can pick up on various forms, shapes and poses.

In addition to rotoscoping virtual production, the team also experimented with live-action stock footage. Being able to use two different types of visual source material baked extra flexibility into the process; teams could mix and match the different methods according to their specific needs or abilities. Fantastical creatures might be captured more easily in virtual production, while a team lacking in their ability to animate lifelike movements may prefer using live-action film as a base. “You get better acting from footage versus a 3D model, but the visual output is ultimately the same,” says Snider-Held.

Much like how that process emulated classic rotoscoping by hand, other ways of integrating AI followed a traditional animation process, albeit with some additional steps here and there. For example, the storyboarding phase is important for visualizing which types of shots or animations are needed for a specific sequence. In addition to pondering that, the team also planned which kinds of AI would be best for generating this or that shot.

Using Stable Diffusion—a kind of generative AI that translates a text prompt into an image—allowed the team to create a large volume of backgrounds that they could swap in and out to test how they looked. “You can explore a lot in this phase,” says Snider-Held.

As for developing backgrounds in particular, “It’s like describing the shot you want to a director of photography,” says Llabata. He was able to test hundreds of different environments, camera angles, artistic styles, lighting and more, all with relative ease.

a grid of landscapes of a house amid mountains and fields

Unlock efficiencies and long-term gains.

The findings above hit on perhaps the biggest gain that a generative AI-powered workflow can provide: greater flexibility throughout the life of a creative project. Being able to generate 60 frames in one minute—rather than one frame in 60 minutes—makes it incredibly easy to pivot or change things up in the blink of an eye.

Monk Thoughts It’s a producer’s dream to be able to create so many assets so flexibly. It redefines linearity in the pipeline because you can always go back and change things.
Joan Llabata headshot

It doesn’t require a sophisticated hardware setup either, further making content creation accessible to teams of all sizes. “You don’t need a giant server or cloud computing,” says Eichhorn. “A reasonably good gaming PC can churn out assets like backdrops quickly.” Still, more complex uses of AI like rotoscoping may require more power.

The flexibility unlocked by integrating generative AI into a team’s pipeline continues to pay dividends beyond the life of a single project. “If you have a project whose scope is really big, the effort and money you spent in that R&D is compounded in value over time,” says Snider-Held, noting that whether a brand wants to make 10 animations or 30, the steps to lay down an AI-powered foundation will be roughly the same.

Experiment to find an approach that suits your needs.

Tools like stable diffusion aren’t meant to replace those in the creative field. “An AI will not achieve anything by itself,” says Llabata. Instead, these products will give teams the ability to chase more ambitious projects with fewer constraints in time and budget. Just consider how closely the creation of the Ancestor Saga trailer follows a traditional animation process, just with more efficiencies baked in. 

Such flexibility afforded by generative AI can go well beyond traditional animation.

Monk Thoughts The merging of data and creativity is something we’re always exploring at Media.Monks, and this technology is going to supercharge that. Imagine using data that we already use for media campaigns to generate hyper-personalized images.
Portrait of Geert Eichhorn

Whatever your use case for generative AI, understand that while building tools from scratch can be challenging, the result is extremely powerful. “Our approach is that if an off-the-shelf tool is mature enough, use it. If not, create it yourself,” says Snider-Held. In addition to ensuring a tool is calibrated for their specific needs, teams who go the bespoke route will also be better poised to future proof as the technology continues to evolve at a rapid pace.

So, think you’re ready to explore what generative AI means for your field? Learn more about the ins and outs of the technology in the latest Labs Report exploring the rapid evolution of digital creation.

Labs.Monks collaborated with animators and illustrators to develop a prototype production workflow that blends traditional animation with cutting-edge AI. artificial intelligence animation prototyping creative technology Experience AI & Emerging Technology Consulting AI Digital transformation

When Speed is Key, MediaMonks Labs Enables Swift, Proactive AI Prototyping

When Speed is Key, MediaMonks Labs Enables Swift, Proactive AI Prototyping

4 min read
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Written by
Labs.Monks

When Speed is Key, MediaMonks Labs Enables Swift, Proactive AI Prototyping

As the COVID-19 pandemic spreads throughout the world and people retreat into their homes to practice social distancing, ingenuity and the need to digitally transform have become more apparent now than ever. Always looking for ways to jump-start innovation, the MediaMonks Labs team has experimented with ways to speed up the development of machine learning-based solutions from prototype to end product, cutting out unnecessary hours of coding to iterate at speed.

“Mental fortitude and being used to curveballs are skills and ways of working that come to the foreground now,” says Geert Eichhorn, Innovation Director at MediaMonks. “We see those eager to adapt come out on top.” Proactively aiming to solve the challenges faced by brands and their everyday audiences, the team recently experimented with a faster way to build and iterate artificial intelligence-driven products and services.

Fun Experiments Can Lead to Proactive Value

The idea behind one such experiment, the Canteen Counter, may seem silly on the surface: determine when the office canteen is less busy, helping the team find the optimal time to go and grab a seat. But the technology behind it provides some learnings for those who aim to solve challenges quickly with off-the-shelf tools.

Here’s how it works. The Canteen Counter’s camera was pointed at the salad bar, capturing the walkway from the entrance to the dishwashers—the most crowded spot in the canteen. The machine learning model detects people in the frame and keeps a count of how many are there to determine when it’s busy and when it isn’t—much like how business listings on Google Maps predict peak versus off-peak hours.

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Of course, now that the team is working from home, there’s little need to keep an eye on the canteen. But one could imagine a similar tool to determine in real time which spaces are safe for social distancing, measured from afar. Is the local park empty enough for some fresh air and exercise? Is the grocery store packed? Ask the AI before you leave!

“I would like to make something that is helpful to people being affected by COVID-19 next,” says Luis Guajardo, Creative Technologist at MediaMonks. “I think that would be an interesting spinoff of this project.” The sentiment shows how such experiments, when executed at speed, can provide necessary solutions to new problems soon after they arise.

Off-the-Shelf Tools Help Teams Plug In, Play and Apply New Learnings

Our Canteen Counter is powered by Google’s Coral, a board that runs optimized TensorFlow models using an Edge TPU chip. To get the jargon out of the way, it essentially lets you employ machine learning offline—a process that typically connects to a cloud, which is why you need a data connection to interact with most digital assistants. The TPU chip (which stands for tensor processing unit) is built to handle the neural network-trained machine learning directly on the hardware.

This not only allows for faster processing, but also increased privacy because data isn’t shared with anyone. Developers may simply take an existing, off-the-shelf machine learning model to quickly optimize to the hardware and the goals of a project. While the steps behind this process are simpler than training a model of your own, there’s still some expertise required in discovering which model best suits your needs—a point made clear with another tool built by Labs that compares computer vision models and the differences between them.

Monk Thoughts What is a canteen counter today could become a camera that tells you something about your posture tomorrow. Anything goes, and it changes by the day.
Portrait of Geert Eichhorn

What the team really likes about Coral is how flexible it is thanks to the TPU chip, which comes in several different boards and modules to easily plug and play. “That means you could use the Coral Board to build initial product prototypes, test models and peripherals, then move into production using only the TPU modules based on your own product specs and electronics and create a robust hardware AI solution,” says Guajardo.

Quicken the Pace of Development to Stay Ahead of Challenges

For the Labs team, tools like Coral have quickened the pace of experimentation and developing new solutions. “The off-the-shelf ML models combined with the Coral board and some creativity can let you build practical solutions in a matter of days,” says Eichhorn. “If it’s not a viable solution you’ll find out as soon as possible, which prevents you from wasting any valuable time and resources.” Eichhorn compares this process to X (formerly Google X), where ideas are broken down as fast as possible to stress test viability.

“At Labs, we jump on new technologies and apply them in new creative ways to solve problems we didn’t know we had, so any project or platform that has as much flexibility as the Canteen Counter is very much up Labs’ alley,” says Eichhorn. “What is a canteen counter today could become a camera that tells you something about your posture tomorrow. Anything goes, and it changes by the day.” He notes that more is being worked on behind the scenes as the team ponders the trend toward livestreaming, the need for showing solidarity, play and interaction while working from home.

It’s worth reflecting on how dramatically the world has changed since we settled on the idea to keep an eye on our workplace canteen through a fun, machine learning experiment. But Eichhorn cautions that in a rush for much-needed solutions, “innovation” can often begin to feel like a buzzword. “What we do differently is that we can actually build, be practical, execute, and make it work.”

Extraordinary times call for extraordinary solutions.

Focused on solutions that are both useful and practical, the MediaMonks Labs team shares its approach to rapidly prototyping machine learning-based solutions. When Speed is Key, MediaMonks Labs Enables Swift, Proactive AI Prototyping By cutting out unnecessary coding hours, MediaMonks Labs builds solutions at speed.
Machine learning artificial intelligence mediamonks labs prototyping innovation google coral coral board

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