Tag Archives: machine learning

AI and deep learning at NAB 2019

By Tim Nagle

If you’ve been there, you know. Attending NAB can be both exciting and a chore. The vast show floor spreads across three massive halls and several hotels, and it will challenge even the most comfortable shoes. With an engineering background and my daily position as a Flame artist, I am definitely a gear-head, but I feel I can hardly claim that title at these events.

Here are some of my takeaways from the show this year…

Tim Nagle

8K
Having listened to the rumor mill, this year’s event promised to be exciting. And for me, it did not disappoint. First impressions: 8K infrastructure is clearly the goal of the manufacturers. Massive data rates and more Ks are becoming the norm. Everybody seemed to have an 8K workflow announcement. As a Flame artist, I’m not exactly looking forward to working on 8K plates. Sure, it is a glorious number of pixels, but the challenges are very real. While this may be the hot topic of the show, the fact that it is on the horizon further solidifies the need for the industry at large to have a solid 4K infrastructure. Hey, maybe we can even stop delivering SD content soon? All kidding aside, the systems and infrastructure elements being designed are quite impressive. Seeing storage solutions that can read and write at these astronomical speeds is just jaw dropping.

Young Attendees
Attendance remained relatively stable this year, but what I did notice was a lot of young faces making their way around the halls. It seemed like high school and university students were able to take advantage of interfacing with manufacturers, as well as some great educational sessions. This is exciting, as I really enjoy watching young creatives get the opportunity to express themselves in their work and make the rest of us think a little differently.

Blackmagic Resolve 16

AI/Deep Learning
Speaking of the future, AI and deep learning algorithms are being implemented into many parts of our industry, and this is definitely something to watch for. The possibilities to increase productivity are real, but these technologies are still relatively new and need time to mature. Some of the post apps taking advantage of these algorithms come from Blackmagic, Autodesk and Adobe.

At the show, Blackmagic announced their Neural Engine AI processing, which is integrated into DaVinci Resolve 16 for facial recognition, speed warp estimation and object removal, to name just a few. These features will add to the productivity of this software, further claiming its place among the usual suspects for more than just color correction.

Flame 2020

The Autodesk Flame team has implemented deep learning in to their app as well. It portends really impressive uses for retouching and relighting, as well as creating depth maps of scenes. Autodesk demoed a shot of a woman on the beach, with no real key light possibility and very flat, diffused lighting in general. With a few nodes, they were able to relight her face to create a sense of depth and lighting direction. This same technique can be used for skin retouch as well, which is very useful in my everyday work.

Adobe has also been working on their implementation of AI with the integration of Sensei. In After Effects, the content-aware algorithms will help to re-texture surfaces, remove objects and edge blend when there isn’t a lot of texture to pull from. Watching a demo artist move through a few shots, removing cars and people from plates with relative ease and decent results, was impressive.

These demos have all made their way online, and I encourage everyone to watch. Seeing where we are headed is quite exciting. We are on our way to these tools being very accurate and useful in everyday situations, but they are all very much a work in progress. Good news, we still have jobs. The robots haven’t replaced us yet.


Tim Nagle is a Flame artist at Dallas-based Lucky Post.

NAB 2019: First impressions

By Mike McCarthy

There are always a slew of new product announcements during the week of NAB, and this year was no different. As a Premiere editor, the developments from Adobe are usually the ones most relevant to my work and life. Similar to last year, Adobe was able to get their software updates released a week before NAB, instead of for eventual release months later.

The biggest new feature in the Adobe Creative Cloud apps is After Effects’ new “Content Aware Fill” for video. This will use AI to generate image data to automatically replace a masked area of video, based on surrounding pixels and surrounding frames. This functionality has been available in Photoshop for a while, but the challenge of bringing that to video is not just processing lots of frames but keeping the replaced area looking consistent across the changing frames so it doesn’t stand out over time.

The other key part to this process is mask tracking, since masking the desired area is the first step in that process. Certain advances have been made here, but based on tech demos I saw at Adobe Max, more is still to come, and that is what will truly unlock the power of AI that they are trying to tap here. To be honest, I have been a bit skeptical of how much AI will impact film production workflows, since AI-powered editing has been terrible, but AI-powered VFX work seems much more promising.

Adobe’s other apps got new features as well, with Premiere Pro adding Free-Form bins for visually sorting through assets in the project panel. This affects me less, as I do more polishing than initial assembly when I’m using Premiere. They also improved playback performance for Red files, acceleration with multiple GPUs and certain 10-bit codecs. Character Animator got a better puppet rigging system, and Audition got AI-powered auto-ducking tools for automated track mixing.

Blackmagic
Elsewhere, Blackmagic announced a new version of Resolve, as expected. Blackmagic RAW is supported on a number of new products, but I am not holding my breath to use it in Adobe apps anytime soon, similar to ProRes RAW. (I am just happy to have regular ProRes output available on my PC now.) They also announced a new 8K Hyperdeck product that records quad 12G SDI to HEVC files. While I don’t think that 8K will replace 4K television or cinema delivery anytime soon, there are legitimate markets that need 8K resolution assets. Surround video and VR would be one, as would live background screening instead of greenscreening for composite shots. No image replacement in post, as it is capturing in-camera, and your foreground objects are accurately “lit” by the screens. I expect my next major feature will be produced with that method, but the resolution wasn’t there for the director to use that technology for the one I am working on now (enter 8K…).

AJA
AJA was showing off the new Ki Pro Go, which records up to four separate HD inputs to H.264 on USB drives. I assume this is intended for dedicated ISO recording of every channel of a live-switched event or any other multicam shoot. Each channel can record up to 1080p60 at 10-bit color to H264 files in MP4 or MOV and up to 25Mb.

HP
HP had one of their existing Z8 workstations on display, demonstrating the possibilities that will be available once Intel releases their upcoming DIMM-based Optane persistent memory technology to the market. I have loosely followed the Optane story for quite a while, but had not envisioned this impacting my workflow at all in the near future due to software limitations. But HP claims that there will be options to treat Optane just like system memory (increasing capacity at the expense of speed) or as SSD drive space (with DIMM slots having much lower latency to the CPU than any other option). So I will be looking forward to testing it out once it becomes available.

Dell
Dell was showing off their relatively new 49-inch double-wide curved display. The 4919DW has a resolution of 5120×1440, making it equivalent to two 27-inch QHD displays side by side. I find that 32:9 aspect ratio to be a bit much for my tastes, with 21:9 being my preference, but I am sure there are many users who will want the extra width.

Digital Anarchy
I also had a chat with the people at Digital Anarchy about their Premiere Pro-integrated Transcriptive audio transcription engine. Having spent the last three months editing a movie that is split between English and Mandarin dialogue, needing to be fully subtitled in both directions, I can see the value in their tool-set. It harnesses the power of AI-powered transcription engines online and integrates the results back into your Premiere sequence, creating an accurate script as you edit the processed clips. In my case, I would still have to handle the translations separately once I had the Mandarin text, but this would allow our non-Mandarin speaking team members to edit the Mandarin assets in the movie. And it will be even more useful when it comes to creating explicit closed captioning and subtitles, which we have been doing manually on our current project. I may post further info on that product once I have had a chance to test it out myself.

Summing Up
There were three halls of other products to look through and check out, but overall, I was a bit underwhelmed at the lack of true innovation I found at the show this year.

Full disclosure, I was only able to attend for the first two days of the exhibition, so I may have overlooked something significant. But based on what I did see, there isn’t much else that I am excited to try out or that I expect to have much of a serious impact on how I do my various jobs.

It feels like most of the new things we are seeing are merely commoditized versions of products that may originally have been truly innovative when they were initially released, but now are just slightly more fleshed out versions over time.

There seems to be much less pioneering of truly new technology and more repackaging of existing technologies into other products. I used to come to NAB to see all the flashy new technologies and products, but now it feels like the main thing I am doing there is a series of annual face-to-face meetings, and that’s not necessarily a bad thing.

Until next year…


Mike McCarthy is an online editor/workflow consultant with over 10 years of experience on feature films and commercials. He has been involved in pioneering new solutions for tapeless workflows, DSLR filmmaking and multi-screen and surround video experiences. Check out his site.

Dell updates Precision 7000 Series workstation line

Dell has updated its Precision 7920 and 7820 towers and Precision 7920 rack workstations to target the media and entertainment industry. Enhancements include processing of large data workloads, AI capabilities, hot-swappable drives, a tool-less external power supply and a flexible 2U rack form factor that boosts cooling, noise reduction and space savings.

Both the Dell Precision 7920 and 7820 towers will be available with the new 2nd Gen Intel Xeon Scalable processors and Nvidia Quadro RTX graphic options to deliver enhanced performance for applications with large datasets, including enhancements for artificial intelligence and machine learning workloads. All Precision workstations come equipped with the Dell Precision Optimizer. The Dell Precision Optimizer Premium is available at an additional cost. This feature uses AI-based technology to tune the workstation based on how it is being used.

In addition, the Precision workstations now feature a multichannel thermal design for advanced cooling and acoustics. An externally accessible tool-less power supply and FlexBays for lockable, hot-swappable drives are also included.

For users needing high-security, remotely accessible 1:1 workstation performance, the updated Dell Precision 7920 rack workstation delivers the same performance and scalability of the Dell Precision 7920 tower in a 2U rack form factor. This rack workstation is targeted to OEMs and users who need to locate their compute resources and valuable data in central environments. This option can save space and help reduce noise and heat, while providing secure remote access to external employees and contractors.

Configuration options will include the recently announced 2nd Gen Intel Xeon Scalable processors, built for advanced workstation professionals, with up to 28 cores, 56 threads and 3TB DDR4 RDIMM per socket. The workstations will also support Intel Deep Learning Boost, a new set of Intel AVX-512 instructions.

The Precision 7000 Series workstations will be available in May with high-performance storage capacity options, including up to 120TB/96TB of Enterprise SATA HDD and up to 16TB of PCIe NVMe SSDs.

Video: Machine learning with Digital Domain’s Doug Roble

Just prior to NAB, postPerspective’s Randi Altman caught up with Digital Domain’s senior director of software R&D, Doug Roble, to talk machine learning.

Roble is on a panel on the Monday of NAB 2019 called “Influencers in AI: Companies Accelerating the Future.” It’s being moderated by Google’s technical director for media, Jeff Kember, and features Roble along with
Autodesk’s Evan Atherton, Nvidia’s Rick Champagne, Warner Bros’ Greg Gewickey, Story Tech/Television Academy’s Lori Schwartz.

In our conversation with Roble, he talks about how Digital Domain has been using machine learning in visual effects for a couple of years. He points to the movie Avengers and the character Thanos, which they worked on.

A lot of that character’s facial motion was done with a variety of machine learning techniques. Since then, Digital Domain has pushed that technology further, taking the machine learning aspect and putting it on realtime digital humans — including Doug Roble.

Watch our conversation and find out more…

Autodesk’s Flame 2020 features machine learning tools

Autodesk’s new Flame 2020 offers a new machine-learning-powered feature set with a host of new capabilities for Flame artists working in VFX, color grading, look development or finishing. This latest update will be showcased at the upcoming NAB Show.

Advancements in computer vision, photogrammetry and machine learning have made it possible to extract motion vectors, Z depth and 3D normals based on software analysis of digital stills or image sequences. The Flame 2020 release adds built-in machine learning analysis algorithms to isolate and modify common objects in moving footage, dramatically accelerating VFX and compositing workflows.

New creative tools include:
· Z-Depth Map Generator— Enables Z-depth map extraction analysis using machine learning for live-action scene depth reclamation. This allows artists doing color grading or look development to quickly analyze a shot and apply effects accurately based on distance from camera.
· Human Face Normal Map Generator— Since all human faces have common recognizable features (relative distance between eyes, nose, location of mouth) machine learning algorithms can be trained to find these patterns. This tool can be used to simplify accurate color adjustment, relighting and digital cosmetic/beauty retouching.
· Refraction— With this feature, a 3D object can now refract, distorting background objects based on its surface material characteristics. To achieve convincing transparency through glass, ice, windshields and more, the index of refraction can be set to an accurate approximation of real-world material light refraction.

Productivity updates include:
· Automatic Background Reactor— Immediately after modifying a shot, this mode is triggered, sending jobs to process. Accelerated, automated background rendering allows Flame artists to keep projects moving using GPU and system capacity to its fullest. This feature is available on Linux only, and can function on a single GPU.
· Simpler UX in Core Areas— A new expanded full-width UX layout for MasterGrade, Image surface and several Map User interfaces, are now available, allowing for easier discoverability and accessibility to key tools.
· Manager for Action, Image, Gmask—A simplified list schematic view, Manager makes it easier to add, organize and adjust video layers and objects in the 3D environment.
· Open FX Support—Flame, Flare and Flame Assist version 2020 now include comprehensive support for industry-standard Open FX creative plugins such as Batch/BFX nodes or on the Flame timeline.
· Cryptomatte Support—Available in Flame and Flare, support for the Cryptomatte open source advanced rendering technique offers a new way to pack alpha channels for every object in a 3D rendered scene.

For single-user licenses, Linux customers can now opt for monthly, yearly and three-year single user licensing options. Customers with an existing Mac-only single user license can transfer their license to run Flame on Linux.
Flame, Flare, Flame Assist and Lustre 2020 will be available on April 16, 2019 at no additional cost to customers with a current Flame Family 2019 subscription. Pricing details can be found at the Autodesk website.

AI for M&E: Should you take the leap?

By Nick Gold

In Hollywood, the promise of artificial intelligence is all the rage. Who wouldn’t want a technology that adds the magic of AI to smarter computers for an instant solution to tedious, time-intensive problems? With artificial intelligence, anyone with abundant rich media assets can easily churn out more revenue or cut costs, while simplifying operations … or so we’re told.

If you attended IBC, you probably already heard the pitch: “It’s an ‘easy’ button that’s simple to add to the workflow and foolproof to operate, turning your massive amounts of uncategorized footage into metadata.”

But should you take the leap? Before you sign on the dotted line, take a closer look at the technology behind AI and what it can — and can’t — do for you.

First, it’s important to understand the bigger picture of artificial intelligence in today’s marketplace. Taking unstructured data and generating relevant metadata from it is something that other industries have been doing for some time. In fact, many of the tools we embrace today started off in other industries. But unlike banking, finance or healthcare, our industry prioritizes creativity, which is why we have always shied away from tools that automate. The idea that we can rely on the same technology as a hedge fund manager just doesn’t sit well with many people in our industry, and for good reason.

Nick Gold talks AI for a UCLA Annex panel.

In the media and entertainment industry, we’re looking for various types of metadata that could include a transcript of spoken words, important events within a period of time or information about the production (e.g., people, location, props), and currently there’s no single machine-learning algorithm that will solve for all these types of metadata parameters. For that reason, the best starting point is to define your problems and identify which machine learning tools may be able to solve them. Expecting to parse reams of untagged, uncategorized and unstructured media data is unrealistic until you know what you’re looking for.

What works for M&E?
AI has become pretty good at solving some specific problems for our industry. Speech-to-text is one of them. With AI, extracting data from a generally accurate transcription offers an automated solution that saves time. However, it’s important to note that AI tools still have limitations. An AI tool, known as “sentiment analysis,” could theoretically look for the emotional undertones described in spoken word, but it first requires another tool to generate a transcript for analysis.

But no matter how good the algorithms are, they won’t give you the qualitative data that a human observer would provide, such as the emotions expressed through body language. They won’t tell you the facial expressions of the people being spoken to, or the tone of voice, pacing and volume level of the speaker, or what is conveyed by a sarcastic tone or a wry expression. There are sentiment analysis engines that try to do this, but breaking down the components ensures the parameters you need will be addressed and solved.

Another task at which machine learning has progressed significantly is logo recognition. Certain engines are good at finding, for example, all the images with a Coke logo in 10,000 hours of video. That’s impressive and quite useful, but it’s another story if you want to also find footage of two people drinking what are clearly Coke-shaped bottles where the logo is obscured. That’s because machine-learning engines tend to have a narrow focus, which goes back to the need to define very specifically what you hope to get from it.

There are a bevy of algorithms and engines out there. If you license a service that will find a specific logo, then you haven’t solved your problem for finding objects that represent the product as well. Even with the right engine, you’ve got to think about how this information fits in your pipeline, and there are a lot of workflow questions to be explored.

Let’s say you’ve generated speech-to-text with audio media, but have you figured out how someone can search the results? There are several options. Sometimes vendors have their own front end for searching. Others may offer an export option from one engine into a MAM that you either already have on-premise or plan to purchase. There are also vendors that don’t provide machine learning themselves but act as a third-party service organizing the engines.

It’s important to remember that none of these AI solutions are accurate all the time. You might get a nudity detection filter, for example, but these vendors rely on probabilistic results. If having one nude image slip through is a huge problem for your company, then machine learning alone isn’t the right solution for you. It’s important to understand whether occasional inaccuracies will be acceptable or deal breakers for your company. Testing samples of your core content in different scenarios for which you need to solve becomes another crucial step. And many vendors are happy to test footage in their systems.

Although machine learning is still in its nascent stages, there is a lot of interest in learning how to make it work in the media workflow. It can do some magical things, but it’s not a magic “easy” button (yet, anyway). Exploring the options and understanding in detail what you need goes hand-in-hand with finding the right solution to integrate with your workflow.


Nick Gold is lead technologist for Baltimore’s Chesapeake Systems, which specializes in M&E workflows and solutions for the creation, distribution and preservation of content. Active in both SMPTE and the Association of Moving Image Archivists (AMIA), Gold speaks on a range of topics. He also co-hosts the Workflow Show Podcast.
 

Dell EMC’s ‘Ready Solutions for AI’ now available

Dell EMC has made available its new Ready Solutions for AI, with specialized designs for Machine Learning with Hadoop and Deep Learning with Nvidia.

Dell EMC Ready Solutions for AI eliminate the need for organizations to individually source and piece together their own solutions. They offer a Dell EMC-designed and validated set of best-of-breed technologies for software — including AI frameworks and libraries — with compute, networking and storage. Dell EMC’s portfolio of services include consulting, deployment, support and education.

Dell EMC’s Data Science Provisioning Portal offers an intuitive GUI that provides self-service access to hardware resources and a comprehensive set of AI libraries and frameworks, such as Caffe and TensorFlow. This reduces the steps it takes to configure a data scientist’s workspace to five clicks. Ready Solutions for AI’s distributed, scalable architecture offers the capacity and throughput of Dell EMC Isilon’s All-Flash scale-out design, which can improve model accuracy with fast access to larger data sets.

Dell EMC Ready Solutions for AI: Deep Learning with Nvidia solutions are built around Dell EMC PowerEdge servers with Nvidia Tesla V100 Tensor Core GPUs. Key features include Dell EMC PowerEdge R740xd and C4140 servers with four Nvidia Tesla V100 SXM2 Tensor Core GPUs; Dell EMC Isilon F800 All-Flash Scale-out NAS storage; and Bright Cluster Manager for Data Science in combination with the Dell EMC Data Science Provisioning Portal.

Dell EMC Ready Solutions for AI: Machine Learning with Hadoop includes an optimized solution stack, along with data science and framework optimization to get up and running quickly, and it allows expansion of existing Hadoop environments for machine learning.

Key features include Dell EMC PowerEdge R640 and R740xd servers; Cloudera Data Science Workbench for self-service data science for the enterprise; the Apache Spark open source unified data analytics engine; and the Dell EMC Data Science Provisioning Engine, which provides preconfigured containers that give data scientists access to the Intel BigDL distributed deep learning library on the Spark framework.

New Dell EMC Consulting services are available to help customers implement and operationalize the Ready Solution technologies and AI libraries, and scale their data engineering and data science capabilities. Dell EMC Education Services offers courses and certifications on data science and advanced analytics and workshops on machine learning in collaboration with Nvidia.

Dell makes updates to its Precision mobile workstation line

Recently, Dell made updates to its line of Precision mobile workstations targeting the media and entertainment industries. The Dell Precision 7730 and 7530 mobile workstations feature the latest eighth-generation IntelCore and Xeon processors, AMD Radeon WX and Nvidia Quadro professional graphics, 3200MHz SuperSpeed memory and memory capacity up to 128GB.

The Dell Precision 7530 is a 15-inch VR-ready mobile workstation with large PCIe SSD storage capacity, especially for a 15-inch mobile workstation — up to 6TB. Dell says the 7730 enables new uses such as AI and machine learning development and edge inference systems.

Also new is the 15-inch Dell Precision 5530 two-in-one, which targets content creation and editing and features a very thin design. A flexible 360-degree hinge enables multiple modes of interaction, including support for touch and pen. It features the next-generation InfinityEdge 4K Ultra HD display. The Dell Premium pen offers precise pressure sensitivity (4,096 pressure points), tilt functionality and low latency for an experience that is reminiscent of drawing on paper. The new MagLev keyboard design reduces keyboard thickness “without compromising critical keyboard shortcuts in content creation workflows,” and ultra-thin GORE Thermal Insulation keeps the system cool.

This workstation weighs 3.9 pounds and delivers next-generation professional graphics up to Nvidia Quadro P2000. With enhanced 2666MHz memory speeds up to 32GB, users can accelerate their complicated workflows. And with up to 4TB of SSD storage, users can access, transfer and store large 3D, video and multimedia files quickly and easily.

The fully customizable 15-inch Dell Precision 3530 mobile workstation features eighth-generation Intel Core and next-generation Xeon processors, memory speeds up to 2666MHz and Nvidia Quadro P600 professional graphics. It also features a 92WHr battery and wide range of ports, including HDMI 2.0, Thunderbolt and VGA.

GTC embraces machine learning and AI

By Mike McCarthy

I had the opportunity to attend GTC 2018, Nvidia‘s 9th annual technology conference in San Jose this week. GTC stands for GPU Technology Conference, and GPU stands for graphics processing unit, but graphics makes up a relatively small portion of the show at this point. The majority of the sessions and exhibitors are focused on machine learning and artificial intelligence.

And the majority of the graphics developments are centered around analyzing imagery, not generating it. Whether that is classifying photos on Pinterest or giving autonomous vehicles machine vision, it is based on the capability of computers to understand the content of an image. Now DriveSim, Nvidia’s new simulator for virtually testing autonomous drive software, dynamically creates imagery for the other system in the Constellation pair of servers to analyze and respond to, but that is entirely machine-to-machine imagery communication.

The main exception to this non-visual usage trend is Nvidia RTX, which allows raytracing to be rendered in realtime on GPUs. RTX can be used through Nvidia’s OptiX API, as well as Microsoft’s DirectX RayTracing API, and eventually through the open source Vulkan cross-platform graphics solution. It integrates with Nvidia’s AI Denoiser to use predictive rendering to further accelerate performance, and can be used in VR applications as well.

Nvidia RTX was first announced at the Game Developers Conference last week, but the first hardware to run it was just announced here at GTC, in the form of the new Quadro GV100. This $9,000 card replaces the existing Pascal-based GP100 with a Volta-based solution. It retains the same PCIe form factor, the quad DisplayPort 1.4 outputs and the NV-Link bridge to pair two cards at 200GB/s, but it jumps the GPU RAM per card from 16GB to 32GB of HBM2 memory. The GP100 was the first Quadro offering since the K6000 to support double-precision compute processing at full speed, and the increase from 3,584 to 5,120 CUDA cores should provide a 40% increase in performance, before you even look at the benefits of the 640 Tensor Cores.

Hopefully, we will see simpler versions of the Volta chip making their way into a broader array of more budget-conscious GPU options in the near future. The fact that the new Nvidia RTX technology is stated to require Volta architecture CPUs leads me to believe that they must be right on the horizon.

Nvidia also announced a new all-in-one GPU supercomputer — the DGX-2 supports twice as many Tesla V100 GPUs (16) with twice as much RAM each (32GB) compared to the existing DGX-1. This provides 81920 CUDA cores addressing 512GB of HBM2 memory, over a fabric of new NV-Link switches, as well as dual Xeon CPUs, Infiniband or 100GbE connectivity, and 32TB of SSD storage. This $400K supercomputer is marketed as the world’s largest GPU.

Nvidia and their partners had a number of cars and trucks on display throughout the show, showcasing various pieces of technology that are being developed to aid in the pursuit of autonomous vehicles.

Also on display in the category of “actually graphics related” was the new Max-Q version of the mobile Quadro P4000, which is integrated into PNY’s first mobile workstation, the Prevail Pro. Besides supporting professional VR applications, the HDMI and dual DisplayPort outputs allow a total of three external displays up to 4K each. It isn’t the smallest or lightest 15-inch laptop, but it is the only system under 17 inches I am aware of that supports the P4000, which is considered the minimum spec for professional VR implementation.

There are, of course, lots of other vendors exhibiting their products at GTC. I had the opportunity to watch 8K stereo 360 video playing off of a laptop with an external GPU. I also tried out the VRHero 5K Plus enterprise-level HMD, which brings the VR experience to whole other level. Much more affordable is TP-Cast’s $300 wireless upgrade Vive and Rift HMDs, the first of many untethered VR solutions. HTC has also recently announced the Vive Pro, which will be available in April for $800. It increases the resolution by 1/3 in both dimensions to 2880×1600 total, and moves from HDMI to DisplayPort 1.2 and USB-C. Besides VR products, they also had all sorts of robots in various forms on display.

Clearly the world of GPUs has extended far beyond the scope of accelerating computer graphics generation, and Nvidia is leading the way in bringing massive information processing to a variety of new and innovative applications. And if that leads us to hardware that can someday raytrace in realtime at 8K in VR, then I suppose everyone wins.


Mike McCarthy is an online editor/workflow consultant with 10 years of experience on feature films and commercials. He has been involved in pioneering new solutions for tapeless workflows, DSLR filmmaking and multi-screen and surround video experiences. Check out his site.