Hacker-Powered Security for Startups
You are laser-focused on building the best product, and growing fast without sacrificing quality. But growth can be crippled if customers sense a risk to their data. Learn how hyper-growth organizations like Sumo Logic, Flexport, and Alien Vault are accelerating innovation without compromising their product or security. Download E-Book.
Nvidia on Monday announced a breakthrough in 3D rendering research that may have far-reaching ramifications for future virtual worlds.
A team led by Nvidia Vice President Bryan Catanzaro discovered a way to use a neural network to render synthetic 3D environments in real time, using a model trained on real-world videos.
Now, each object in a virtual world has to be modeled individually. With Nvidia's technology, worlds can be populated with objects "learned" from video input.
Nvidia's technology offers the potential to quickly create virtual worlds for gaming, automotive, architecture, robotics or virtual reality. The network can, for example, generate interactive scenes based on real-world locations or show consumers dancing like their favorite pop stars.
To your great surprise, the concept of Head Mounted Display is also not a new idea. The first head-mounted display was developed around 1960’s. the Telesphere mask was the first example of a head-mounted display, which provided 3D stereoscopic and wide vision with sound.
"Nvidia has been inventing new ways to generate interactive graphics for 25 years, and this is the first time we can do so with a neural network," Catanzaro said.
"Neural networks -- specifically generative models -- will change how graphics are created," he added. "This will enable developers to create new scenes at a fraction of the traditional cost."
Learning From Video
The research currently is on display at the NeurIPS conference in Montreal, Canada, a show for artificial intelligence researchers.
Nvidia's team created a simple driving game for the conference that allows attendees to interactively navigate an AI-generated environment.
The virtual urban environment rendered by a neural network was trained on videos of real-life urban environments. The network learned to model the appearance of the world, including lighting, materials and their dynamics.
Since the output is synthetically generated, a scene easily can be edited to remove, modify or add objects.
The State of VR in the Early 2000s. After so many capable devices on the market and so many let downs that didn’t truly capture the audience they deserved, virtual reality didn’t see much development in the early 2000s. Virtual Reality was at the background in the development of new technology. It took a step back, letting personal devices, such as computers, laptops, iPods, smartphones and tablets take over, which may very well have been the right step. With the development of new technologies, a new door was opened for virtual reality, because now head-tracking and capable displays were cheaper than ever before. However, it wasn’t before one start-up company mentioned the idea, that Virtual Reality truly took off on the consumer’s market.
Reducing Labor Overhead
Rendering 3D graphics is a labor-intensive process right now. Nvidia's technology could change that in the future.
"This is cool because it's using deep learning to cut down on what has traditionally been a very manual and resource-intensive activity," said Tuong Nguyen, an analyst with Gartner , a research and advisory company based in Stamford, Connecticut.
"This has applications wherever 3D graphics are used -- video games, augmented reality, virtual reality, TV and movies," he told TechNewsWorld.
"It frees up the graphic professionals' time so they can do other things, such as improve on a scene's quality with additional details," Nguyen added. "The idea is to lay the foundation, or at least do a lot of the heavy lifting, so you can spend more time and energy on making a project stand out in many other different ways."
"Developers and users of virtual environments will especially benefit from the new technology," noted Tamar Shinar, an assistant professor in the department of computer science and engineering at the University of California, Riverside .
"It potentially replaces the laborious process of designing the appearance of a virtual world, and expensive methods to render it photorealistically, with a process based on video input and computation at interactive rates," she told TechNewsWorld.
"It enables the rendering of virtual environments from video data," Shinar continued. "This novel approach to interactive rendering of virtual environments opens many possibilities for interactive applications such as games, telecommunication and training simulators."
The Rise of Oculus Rift. You’ve probably already heard the story, but in the 2010s, Oculus VR, a start-up company decided to release a Kickstarter project for their Oculus Rift virtual reality goggles. Little known to them, the device kickstarted the industry of virtual reality again.
Competition for Hollywood
By taking the drudgery out of 3D rendering, Nvidia's technology also could bring into the market players that previously had been priced out of it.
"Currently, the creation of 3D content and scenes has been very labor-intensive and limited to companies with big budgets -- primarily games companies," said Bill Orner, a senior member of IEEE , a technical professional organization with corporate headquarters in New York City.
"This deep learning model will enable other industries that don't have 'Hollywood' budgets to create 3D interactive tools," he told TechNewsWorld.
"One thing that artificial intelligence and machine learning does is take the human out of some of the process," explained Michael Goodman, director for digital media in the Newton, Massachusetts, office of Strategy Analytics , a research, advisory and analytics firm.
What most people don't realize is that many of the innovations in our lives are a direct result of military and aerospace applications and other government-funded research. As a result, technology is moving so fast that it is impossible to see all the potential consequences, much yet the applications, in advance.
"That allows a lot of money to be saved," he told TechNewsWorld.
That could be good news for content producers for virtual reality headsets.
"Currently, VR content creation is prohibitively costly, and it is difficult to create the kinds of experiences consumers are looking for," explained Kristen Hanich, a research analyst with Dallas, Texas-based Parks Assocates, a market research and consulting company specializing in consumer technology products.
"Lowering the barrier to entry should help with the VR industry's content problem -- there's a lack of it," she told TechNewsWorld.
SEGA’s VR Glasses Project That Didn’t Make It. Gaming companies also knew that Virtual Reality was going to become a huge thing in the gaming world. However, while they had the vision, they were lacking the technology we have today. In 1993, at one of the first Consumer Electronics Shows, SEGA announced the Sega VR headset for their Genesis console. The prototype glasses had head tracking, LCD screens in the visor and stereo sound. SEGA’s idea was to release the product for a mere $200 at the time, but technical development issues turned the idea into one of the biggest flops for the infamous gaming company. The product was never released on the market.
Nevertheless, Nvidia has some work to do before the promise of its deep learning technology can be fulfilled.
"While interesting, the technology is in its early stages," observed Parks Associates analyst Craig Leslie Sr.
"The graphics aren't photorealistic, showing the fuzziness encountered with many AI-generated images," he told TechNewsWorld. "It will require significant improvement before it will be considered market ready."
Simulating Bad Behavior
The Nvidia technology also may find a home in the automotive industry.
"A computer's ability to quickly read and understand real-life environments is a critical piece of the self-driving future," said Eric Yaverbaum, CEO of Ericho Communications , a public relations firm in New York City.
"These deep-learning tools could make it easier for cars to make sense of the world around them and navigate their surroundings with less chance for error," he told TechNewsWorld.
"As much as this technology can be used to create rich 3D worlds for gaming technologies," he added, "its application in automobiles seems more profound. It could give AI-driven cars a more accurate computer model that would dramatically improve passenger safety."
Google Cardboard Was a Side Project. The Google Cardboard platform was developed by David Coz and Damien Henry. The two engineers developed the project as part of Google’s”innovation time off” program in which engineers are encouraged to spend 20 percent of their time working on projects that interest them. Thankfully, Google backed the project, and Google Cardboard is now one of the cornerstones of scalable virtual reality.
A problem currently faced by self-driving car developers is simulating real-life driving environments.
"Traffic models now are too simplistic," said Richard Wallace, transportation systems analysis director for the Center for Automotive Research , a nonprofit automotive research organization in Ann Arbor, Michigan.
"Simulation drivers are too well-behaved. We need more realism," he told TechNewsWorld.
"The industry is beginning to realize that these AI systems can never drive enough real-world miles to get all the learning they need to drive a vehicle, so simulation is starting to become prevalent everywhere," Wallace added. "Nvidia's technology could be very useful for that."