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Although it may not immediately be obvious, artificial intelligence forms the cornerstone of functional augmented reality platforms. Does this mean that the generative AI explosion could form the catalyst to cause a struggling AR market to finally reach its potential?
Use cases of AI within augmented reality appear to be cropping up on a weekly basis. The relationship between artificial intelligence and AR has been continuously explored by tech giants like Meta, which launched its own augmented reality engine in 2017 via a mobile app that integrates a local deep neural network to enable machine vision that updates in real-time. It’s this technology that enables Snapchat-style filter overlays to be so accurate, and it’s likely that this operating model will continue to evolve to enhance our AR experience.
Generative AI has the potential to accelerate the competence of augmented reality structures and hardware at a far greater pace. For an industry that’s still suffering from disappointing sales trends through a lack of mainstream adoption, this innovation could be the moment that the AR landscape has been waiting for–-but what exactly can generative AI do for augmented reality?
What Exactly is Generative AI?
Generative AI refers to intelligent programs that have the power to generate their own content based solely on prompts from a user. The finished content can be completely automated and algorithmically created through the use of AI models. Generative AI can even create artwork, text, and visualizations from scratch with now human input whatsoever.
The goal of generative AI is to be capable of using software to create something that’s entirely original, despite it being developed through artificial intelligence, that appears to be entirely human in its structure.
Because of generative AI’s ability to create complex digital renderings from a human prompt, generative AI can be a significant tool in the development of augmented reality programs–and some leading AR-focused firms are already seeking to utilize its potential:
Uniting Generative AI and AR
One example of a leading augmented reality firm making in-roads in generative AI is Snap. According to Snap CEO Evan Spiegel, generative AI has the ability to make the firm’s cameras considerably more powerful today–while these benefits will ultimately be capable of driving the growth of augmented reality as a whole, including AR eyewear.
In terms of how Snap intends to use generative AI to bolster its service, Spiegel envisions the technology will help to improve the resolution and clarity of a Snap after it’s been captured, and could even be used for more cutting-edge and innovative filters.
“We saw a lot of success integrating Snap ML tools into Lens Studio, and it’s really enabled creators to build some incredible things,” Spiegel explained. “We now have 300,000 creators who built more than 3 million lenses in Lens Studio. So, the democratization of these tools, I think, will also be very powerful.”
While this is a tantalizing prospect, it appears that generative AI’s future relationship with augmented reality is where Snap is really looking to excel.
“If we think longer term, five years…this is going to be critical to the growth of augmented reality. So today, if you look at AR, there’s just a real limitation on what you can build in AR because there’s a limited number of 3D models that have been created by artists,” Spiegel said.
“We can use generative AI to help build more of these 3D models very quickly, which can really unlock the full potential of AR and help people make their imagination real in the world.
Creating Unpredictable Worlds with Generative AI
Looking ahead to the age of the metaverse, and how augmented reality has the potential to combine real and virtual worlds, generative AI could hold the key in creating truly unpredictable environments and worlds that can constantly evolve to keep users engaged.
In Mitacs’ project, ‘Generative AI for Dynamic Environment Creation in VR & AR’, creators utilized AI advancements into the creation of 2D environments to enhance and guide the algorithmic generation process. Then, by allowing the AI to respond and learn from the guidance, the project will allow for the creation of environments tailored to the needs of the digital media creation.
This will pave the way for ‘endless’ content, which is a feature that can be far more difficult for human hands to implement within sprawling augmented reality worlds.
Because of the ability for generative AI to identify and learn its surroundings, it means that AR eyewear wearers can benefit from software that can understand where its user is, the interactive elements around them, and their own personal preferences, and create embedded games and landscapes that can turn mundane tasks into engaging experiences.
Because these generative AI models are cloud-based, it’s likely that they won’t inhibit the development of augmented reality eyewear with clunky external hardware, and in the future we could see headsets replicate iconic the easier discrete frames of brands like Flexon and other leading designers.
Although we may still be many years away from this perfect blend of generative AI and augmented reality striking a perfect balance between functionality and an original stream of fresh content, the future certainly looks bright for this powerful relationship.
Although augmented reality eyewear has struggled to find a market in recent years, generative AI has the potential to greatly enhance the user experience for wearers in a way that’s likely to take the technology to the next level. This, coupled with the continued emergence of the metaverse means that we’re likely to see a turnaround in fortunes for the augmented AR eyewear industry.
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