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Category Archives: A.I.

Dell intros two budget-friendly Precision mobile workstations

Dell is offering two new mobile workstations for designers and graphic artists who are looking for entry-level, workstation-class devices — Dell Precision 3540 and 3541. These budget-friendly machines offer a smaller footprint with high performance. Dell’s Precision line has traditionally been used for intensive workloads, such as machine learning and artificial intelligence, and these entry-level versions are designed to allow artists with smaller budgets access to the Precision line’s capabilities.

The Precision 3540 comes with the latest 4-core Intel Core 8th generation processors, up to 32GB of DDR4 memory, AMD Radeon Pro graphics with 2GB of dedicated memory and 2TB of storage. The Precision 3541 will offer additional power, with 9th generation 8-core Intel Core and 6-core Intel Xeon processor options. It will be available with Nvidia Quadro professional graphics with 4GB of dedicated memory. It will also have extreme battery life for on-the-go productivity.

Both models come with Thunderbolt 3 connectivity and optional features to enhance security, such as fingerprint and smartcard readers, an IR camera and a camera shutter. Both models also have a narrow-edge 15.6-inch display. The 3540 model weighs in at 4.04 pounds, and the 3541 model starts at 4.34 pounds.

The Dell Precision 3540 is available now on Dell.com starting at $799, while the Precision 3541 will be available in late May.

NAB 2019: postPerspective Impact Award winners

postPerspective has announced the winners of our Impact Awards from NAB 2019. Seeking to recognize debut products with real-world applications, the postPerspective Impact Awards are voted on by an anonymous judging body made up of respected industry artists and pros (to whom we are very grateful). It’s working pros who are going to be using these new tools — so we let them make the call.

It was fun watching the user ballots come in and discovering which products most impressed our panel of post and production pros. There are no entrance fees for our awards. All that is needed is the ability to impress our voters with products that have the potential to make their workdays easier and their turnarounds faster.

We are grateful for our panel of judges, which grew even larger this year. NAB is exhausting for all, so their willingness to share their product picks and takeaways from the show isn’t taken for granted. These men and women truly care about our industry and sharing information that helps their fellow pros succeed.

To be successful, you can’t operate in a vacuum. We have found that companies who listen to their users, and make changes/additions accordingly, are the ones who get the respect and business of working pros. They aren’t providing tools they think are needed; they are actively asking for feedback. So, congratulations to our winners and keep listening to what your users are telling you — good or bad — because it makes a difference.

The Impact Award winners from NAB 2019 are:

• Adobe for Creative Cloud and After Effects
• Arraiy for DeepTrack with The Future Group’s Pixotope
• ARRI for the Alexa Mini LF
• Avid for Media Composer
• Blackmagic Design for DaVinci Resolve 16
• Frame.io
• HP for the Z6/Z8 workstations
• OpenDrives for Apex, Summit, Ridgeview and Atlas

(All winning products reflect the latest version of the product, as shown at NAB.)

Our judges also provided quotes on specific projects and trends that they expect will have an impact on their workflows.

Said one, “I was struck by the predicted impact of 5G. Verizon is planning to have 5G in 30 cities by end of year. The improved performance could reach 20x speeds. This will enable more leverage using cloud technology.

“Also, AI/ML is said to be the single most transformative technology in our lifetime. Impact will be felt across the board, from personal assistants, medical technology, eliminating repetitive tasks, etc. We already employ AI technology in our post production workflow, which has saved tens of thousands of dollars in the last six months alone.”

Another echoed those thoughts on AI and the cloud as well: “AI is growing up faster than anyone can reasonably productize. It will likely be able to do more than first thought. Post in the cloud may actually start to take hold this year.”

We hope that postPerspective’s Impact Awards give those who weren’t at the show, or who were unable to see it all, a starting point for their research into new gear that might be right for their workflows. Another way to catch up? Watch our extensive video coverage of NAB.

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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…


Nvidia intros Turing-powered Titan RTX

Nvidia has introduced its new Nvidia Titan RTX, a desktop GPU that provides the kind of massive performance needed for creative applications, AI research and data science. Driven by the new Nvidia Turing architecture, Titan RTX — dubbed T-Rex — delivers 130 teraflops of deep learning performance and 11 GigaRays of raytracing performance.

Turing features new RT Cores to accelerate raytracing, plus new multi-precision Tensor Cores for AI training and inferencing. These two engines — along with more powerful compute and enhanced rasterization — will help speed the work of developers, designers and artists across multiple industries.

Designed for computationally demanding applications, Titan RTX combines AI, realtime raytraced graphics, next-gen virtual reality and high-performance computing. It offers the following features and capabilities:
• 576 multi-precision Turing Tensor Cores, providing up to 130 Teraflops of deep learning performance
• 72 Turing RT Cores, delivering up to 11 GigaRays per second of realtime raytracing performance
• 24GB of high-speed GDDR6 memory with 672GB/s of bandwidth — two times the memory of previous-generation Titan GPUs — to fit larger models and datasets
• 100GB/s Nvidia NVLink, which can pair two Titan RTX GPUs to scale memory and compute
• Performance and memory bandwidth sufficient for realtime 8K video editing
• VirtualLink port, which provides the performance and connectivity required by next-gen VR headsets

Titan RTX provides multi-precision Turing Tensor Cores for breakthrough performance from FP32, FP16, INT8 and INT4, allowing faster training and inference of neural networks. It offers twice the memory capacity of previous-generation Titan GPUs, along with NVLink to allow researchers to experiment with larger neural networks and datasets.

Titan RTX accelerates data analytics with RAPIDS. RAPIDS open-source libraries integrate seamlessly with the world’s most popular data science workflows to speed up machine learning.

Titan RTX will be available later in December in the US and Europe for $2,499.


Panasas’ new ActiveStor Ultra targets emerging apps: AI, VR

Panasas has introduced ActiveStor Ultra, the next generation of its high-performance computing storage solution, featuring PanFS 8, a plug-and-play, portable, parallel file system. ActiveStor Ultra offers up to 75GB/s per rack on industry-standard commodity hardware.

ActiveStor Ultra comes as a fully integrated plug-and-play appliance running PanFS 8 on industry-standard hardware. PanFS 8 is the completely re-engineered Panasas parallel file system, which now runs on Linux and features intelligent data placement across three tiers of media — metadata on non-volatile memory express (NVMe), small files on SSDs and large files on HDDs — resulting in optimized performance for all data types.

ActiveStor Ultra is designed to support the complex and varied data sets associated with traditional HPC workloads and emerging applications, such as artificial intelligence (AI), autonomous driving and virtual reality (VR). ActiveStor Ultra’s modular architecture and building-block design enables enterprises to start small and scale linearly. With dock-to-data in one hour, ActiveStor Ultra offers fast data access and virtually eliminates manual intervention to deliver the lowest total cost of ownership (TCO).

ActiveStor Ultra will be available early in the second half of 2019.


Video Coverage: postPerspective Live from SMPTE 2018

The yearly SMPTE Technical Conference and Exhibition was held late last month in Downtown Los Angeles at the Westin Bonaventure Hotel, a new venue for the event.

The conference included presentations that touched on all three of the organization’s “pillars,” which are Standards, Education and Membership.

One of the highlights was a session on autonomous vehicles and how AI and machine learning are making that happen. You might wonder, “What will everyone do with that extra non-driving time?” Well, companies are already thinking of ways to entertain you while you’re on your way to where you need to go. The schedule of sessions and presentations can be found here.

Another highlight at this year’s SMPTE Conference was the Women in Technology lunch, which featured a conversation between Disney’s Kari Grubin and Fem Inc.’s Rachel Payne. Payne is a tech entrepreneur and technology executive who has worked at companies like Google. She was also a Democratic candidate for the 48th Congressional District of California. It was truly inspiring to hear about her path.

Feeling like you might have missed some cool stuff? Well don’t worry, postPerspective’s production crews were capturing interviews with manufacturers in the exhibit hall and with speakers, SMPTE members and so many others throughout the Conference.

A big thank you to AlphaDogs , who shot and posted our videos this year, as well as our other sponsors: Blackmagic Design, The Studio – B&H, LitePanels and Lenovo.

Watch Here!

Quick Chat: AI-based audio mastering

Antoine Rotondo is an audio engineer by trade who has been in the business for the past 17 years. Throughout his career he’s worked in audio across music, film and broadcast, focusing on sound reproduction. After completing college studies in sound design, undergraduate studies in music and music technology, as well as graduate studies in sound recording at McGill University in Montreal, Rotondo went on to work in recording, mixing, producing and mastering.

He is currently an audio engineer at Landr.com, which has released Landr Audio Mastering for Video, which provides professional video editors with AI-based audio mastering capabilities in Adobe Premiere Pro CC.

As an audio engineer how do you feel about AI tools to shortcut the mastering process?
Well first, there’s a myth about how AI and machines can’t possibly make valid decisions in the creative process in a consistent way. There’s actually a huge intersection between artistic intentions and technical solutions where we find many patterns, where people tend to agree and go about things very similarly, often unknowingly. We’ve been building technology around that.

Truth be told there are many tasks in audio mastering that are repetitive and that people don’t necessarily like spending a lot of time on, tasks such as leveling dialogue, music and background elements across multiple segments, or dealing with noise. Everyone’s job gets easier when those tasks become automated.

I see innovation in AI-driven audio mastering as a way to make creators more productive and efficient — not to replace them. It’s now more accessible than ever for amateur and aspiring producers and musicians to learn about mastering and have the resources to professionally polish their work. I think the same will apply to videographers.

What’s the key to making video content sound great?
Great sound quality is effortless and sounds as natural as possible. It’s about creating an experience that keeps the viewer engaged and entertained. It’s also about great communication — delivering a message to your audience and even conveying your artistic vision — all this to impact your audience in the way you intended.

More specifically, audio shouldn’t unintentionally sound muffled, distorted, noisy or erratic. Dialogue and music should shine through. Viewers should never need to change the volume or rewind the content to play something back during the program.

When are the times you’d want to hire an audio mastering engineer and when are the times that projects could solely use an AI-engine for audio mastering?
Mastering engineers are especially important for extremely intricate artistic projects that require direct communication with a producer or artist, including long-form narrative, feature films, television series and also TV commercials. Any project with conceptual sound design will almost always require an engineer to perfect the final master.

Users can truly benefit from AI-driven mastering in short form, non-fiction projects that require clean dialog, reduced background noise and overall leveling. Quick turnaround projects can also use AI mastering to elevate the audio to a more professional level even, when deadlines are tight. AI mastering can now insert itself in the offline creation process, where multiple revisions of a project are sent back and forth, making great sound accessible throughout the entire production cycle.

The other thing to consider is that AI mastering is a great option for video editors who don’t have technical audio expertise themselves, and where lower budgets translate into them having to work on their own. These editors could purchase purpose-built mastering plugins, but they don’t necessarily have the time to learn how to really take advantage of these tools. And even if they did have the time, some would prefer to focus more on all the other aspects of the work that they have to juggle.

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.
 

Adobe updates Creative Cloud

By Brady Betzel

You know it’s almost fall when when pumpkin spice lattes are  back and Adobe announces its annual updates. At this year’s IBC, Adobe had a variety of updates to its Creative Cloud line of apps. From more info on their new editing platform Project Rush to the addition of Characterizer to Character Animator — there are a lot of updates so I’m going to focus on a select few that I think really stand out.

Project Rush

I use Adobe Premiere quite a lot these days; it’s quick and relatively easy to use and will work with pretty much every codec in the universe. In addition, the Dynamic Link between Adobe Premiere Pro and Adobe After Effects is an indispensible feature in my world.

With the 2018 fall updates, Adobe Premiere will be closer to a color tool like Blackmagic’s Resolve with the addition of new hue saturation curves in the Lumetri Color toolset. In Resolve these are some of the most important aspects of the color corrector, and I think that will be the same for Premiere. From Hue vs. Sat, which can help isolate a specific color and desaturate it to Hue vs. Luma, which can help add or subtract brightness values from specific hues and hue ranges — these new color correcting tools further Premiere’s venture into true professional color correction. These new curves will also be available inside of After Effects.

After Effects features many updates, but my favorites are the ability to access depth matte data of 3D elements and the addition of the new JavaScript engine for building expressions.

There is one update that runs across both Premiere and After Effects that seems to be a sleeper update. The improvements to motion graphics templates, if implemented correctly, could be a time and creativity saver for both artists and editors.

AI
Adobe, like many other companies, seem to be diving heavily into the “AI” pool, which is amazing, but… with great power comes great responsibility. While I feel this way and realize others might not, sometimes I don’t want all the work done for me. With new features like Auto Lip Sync and Color Match, editors and creators of all kinds should not lose the forest for the trees. I’m not telling people to ignore these features, but asking that they put a few minutes into discovering how the color of a shot was matched, so you can fix something if it goes wrong. You don’t want to be the editor who says, “Premiere did it” and not have a great solution to fix something when it goes wrong.

What Else?
I would love to see Adobe take a stab at digging up the bones of SpeedGrade and integrating that into the Premiere Pro world as a new tab. Call it Lumetri Grade, or whatever? A page with a more traditional colorist layout and clip organization would go a long way.

In the end, there are plenty of other updates to Adobe’s 2018 Creative Cloud apps, and you can read their blog to find out about other updates.

IBC 2018: Convergence and deep learning

By David Cox

In the 20 years I’ve been traveling to IBC, I’ve tried to seek out new technology, work practices and trends that could benefit my clients and help them be more competitive. One thing that is perennially exciting about this industry is the rapid pace of change. Certainly, from a post production point of view, there is a mini revolution every three years or so. In the past, those revolutions have increased image quality or the efficiency of making those images. The current revolution is to leverage the power and flexibly of cloud computing. But those revolutions haven’t fundamentally changed what we do. The images might have gotten sharper, brighter and easier to produce, but TV is still TV. This year though, there are some fascinating undercurrents that could herald a fundamental shift in the sort of content we create and how we create it.

Games and Media Collide
There is a new convergence on the horizon in our industry. A few years ago, all the talk was about the merge between telecommunications companies and broadcasters, as well as the joining of creative hardware and software for broadcast and film, as both moved to digital.

The new convergence is between media content creation as we know it and the games industry. It was subtle, but technology from gaming was present in many applications around the halls of IBC 2018.

One of the drivers for this is a giant leap forward in the quality of realtime rendering by the two main game engine providers: Unreal and Unity. I program with Unity for interactive applications, and their new HDSRP rendering allows for incredible realism, even when being rendered fast enough for 60+ frames per second. In order to create such high-quality images, those game engines must start with reasonably detailed models. This is a departure from the past, where less detailed models were used for games than were used for film CGI shots, to protect for realtime performance. So, the first clear advantage created by the new realtime renderers is that a film and its inevitable related game can use the same or similar model data.

NCam

Being able to use the same scene data between final CGI and a realtime game engine allows for some interesting applications. Habib Zargarpour from Digital Monarch Media showed a system based on Unity that allows a camera operator to control a virtual camera in realtime within a complex CGI scene. The resulting camera moves feel significantly more real than if they had been keyframed by an animator. The camera operator chases high-speed action, jumps at surprises and reacts to unfolding scenes. The subtleties that these human reactions deliver via minor deviations in the movement of the camera can convey the mood of a scene as much as the design of the scene itself.

NCam was showing the possibilities of augmenting scenes with digital assets, using their system based on the Unreal game engine. The NCam system provides realtime tracking data to specify the position and angle of a freely moving physical camera. This data was being fed to an Unreal game engine, which was then adding in animated digital objects. They were also using an additional ultra-wide-angle camera to capture realtime lighting information from the scene, which was then being passed back to Unreal to be used as a dynamic reflection and lighting map. This ensured that digitally added objects were lit by the physical lights in the realworld scene.

Even a seemingly unrelated (but very enlightening) chat with StreamGuys president Kiriki Delany about all things related to content streaming still referenced gaming technology. Delany talked about their tests to build applications with Unity to provide streaming services in VR headsets.

Unity itself has further aspirations to move into storytelling rather than just gaming. The latest version of Unity features an editing timeline and color grading. This allows scenes to be built and animated, then played out through various virtual cameras to create a linear story. Since those scenes are being rendered in realtime, tweaks to scenes such as positions of objects, lights and material properties are instantly updated.

Game engines not only offer us new ways to create our content, but they are a pathway to create a new type of hybrid entertainment, which sits between a game and a film.

Deep Learning
Other undercurrents at IBC 2018 were the possibilities offered by machine learning and deep learning software. Essentially, a normal computer program is hard wired to give a particular output for a given input. Machine learning allows an algorithm to compare its output to a set of data and adjust itself if the output is not correct. Deep learning extends that principle by using neural network structures to make a vast number of assessments of input data, then draw conclusions and predications from that data.

Real-world applications are already prevalent and are largely related in our industry to processing viewing metrics. For example, Netflix suggests what we might want to watch next by comparing our viewing habits to others with a similar viewing pattern.

But deep learning offers — indeed threatens — much more. Of course, it is understandable to think that, say, delivery drivers might be redundant in a world where autonomous vehicles rule, but surely creative jobs are safe, right? Think again!

IBM was showing how its Watson Studio has used deep learning to provide automated editing highlights packages for sporting events. The process is relatively simple to comprehend, although considerably more complicated in practice. A DL algorithm is trained to scan a video file and “listen” for a cheering crowd. This finds the highlight moment. Another algorithm rewinds back from that to find the logical beginning of that moment, such as the pass forward, the beginning of the volley etc. Taking the score into account helps decide whether that highlight was pivotal to the outcome of the game. Joining all that up creates a highlight package without the services of an editor. This isn’t future stuff. This has been happening over the last year.

BBC R&D was talking about their trials to have DL systems control cameras at sporting events, as they could be trained to follow the “two thirds” framing rule and to spot moments of excitement that justified close-ups.

In post production, manual tasks such as rotoscoping and color matching in color grading could be automated. Even styles for graphics, color and compositing could be “learned” from other projects.

It’s certainly possible to see that deep learning systems could provide a great deal of assistance in the creation of day-to-day media. Tasks that are based on repetitiveness or formula would be the obvious targets. The truth is, much of our industry is repetitive and formulaic. Investors prefer content that is more likely to be a hit, and this leads to replication over innovation.

So, are we heading for “Skynet” and need Arnold to save us? I thought it was very telling that IBM occupied the central stand position in Hall 7 — traditionally the home of the tech companies that have driven creativity in post. Clearly, IBM and its peers are staking their claim. I have no doubt that DL and ML will make massive changes to this industry in the years ahead. Creativity is probably, but not necessarily, the only defence for mere humans to keep a hand in.

That said, at IBC2018 the most popular place for us mere humans to visit was a bar area called The Beach, where we largely drank Heineken. If the ultimate deep learning system is tasked to emulate media people, surely it would create digital alcohol and spend hours talking nonsense, rather than try and take over the media world? So perhaps we have a few years left yet.


David Cox is a VFX compositor and colorist with 20-plus years of experience. He started his career with MPC and The Mill before forming his own London-based post facility. Cox recently created interactive projects with full body motion sensors and 4D/AR experiences.

Our SIGGRAPH 2018 video coverage

SIGGRAPH is always a great place to wander around and learn about new and future technology. You can get see amazing visual effects reels and learn how the work was created by the artists themselves. You can get demos of new products, and you can immerse yourself in a completely digital environment. In short, SIGGRAPH is educational and fun.

If you weren’t able to make it this year, or attended but couldn’t see it all, we would like to invite you to watch our video coverage from the show.

SIGGRAPH 2018

postPerspective Impact Award winners from SIGGRAPH 2018

postPerspective has announced the winners of our Impact Awards from SIGGRAPH 2018 in Vancouver. Seeking to recognize debut products with real-world applications, the postPerspective Impact Awards are voted on by an anonymous judging body made up of respected industry artists and professionals. It’s working pros who are going to be using new tools — so we let them make the call.

The awards honor innovative products and technologies for the visual effects, post production and production industries that will influence the way people work. They celebrate companies that push the boundaries of technology to produce tools that accelerate artistry and actually make users’ working lives easier.

While SIGGRAPH’s focus is on VFX, animation, VR/AR, AI and the like, the types of gear they have on display vary. Some are suited for graphics and animation, while others have uses that slide into post production, which makes these SIGGRAPH Impact Awards doubly interesting.

The winners are as follows:

postPerspective Impact Award — SIGGRAPH 2018 MVP Winner:

They generated a lot of buzz at the show, as well as a lot of votes from our team of judges, so our MVP Impact Award goes to Nvidia for its Quadro RTX raytracing GPU.

postPerspective Impact Awards — SIGGRAPH 2018 Winners:

  • Maxon for its Cinema 4D R20 3D design and animation software.
  • StarVR for its StarVR One headset with integrated eye tracking.

postPerspective Impact Awards — SIGGRAPH 2018 Horizon Winners:

This year we have started a new Imapct Award category. Our Horizon Award celebrates the next wave of impactful products being previewed at a particular show. At SIGGRAPH, the winners were:

  • Allegorithmic for its Substance Alchemist tool powered by AI.
  • OTOY and Epic Games for their OctaneRender 2019 integration with UnrealEngine 4.

And while these products and companies didn’t win enough votes for an award, our voters believe they do deserve a mention and your attention: Wrnch, Google Lightfields, Microsoft Mixed Reality Capture and Microsoft Cognitive Services integration with PixStor.

 

DeepMotion’s Neuron cloud app trains digital characters using AI

DeepMotion has launched DeepMotion Neuron, the first tool for completely procedural, physical character animation, for presale. The cloud application trains digital characters to develop physical intelligence using advanced artificial intelligence (AI), physics and deep learning. With guidance and practice, digital characters can now achieve adaptive motor control just as humans do, in turn allowing animators and developers to create more lifelike and responsive animations than those possible using traditional methods.

DeepMotion Neuron is a behavior-as-a-service platform that developers can use to upload and train their own 3D characters, choosing from hundreds of interactive motions available via an online library. Neuron will enable content creators to tell more immersive stories by adding responsive actors to games and experiences. By handling large portions of technical animation automatically, the service also will free up time for artists to focus on expressive details.

DeepMotion Neuron is built on techniques identified by researchers from DeepMotion and Carnegie Mellon University who studied the application of reinforcement learning to the growing domain of sports simulation, specifically basketball, where real-world human motor intelligence is at its peak. After training and optimization, the researchers’ characters were able to perform interactive ball-handling skills in real-time simulation. The same technology used to teach digital actors how to dribble can be applied to any physical movement using Neuron.

DeepMotion Neuron’s cloud platform is slated for release in Q4 of 2018. During the DeepMotion Neuron prelaunch, developers and animators can register on the DeepMotion website for early access and discounts.

Epic Games launches Unreal Engine 4.20

Epic Games has introduced Unreal Engine 4.20, which allows developers to build even more realistic characters and immersive environments across games, film and TV, VR/AR/MR and enterprise applications. The Unreal Engine 4.20 release combines the latest realtime rendering advancements with improved creative tools, making it even easier to ship games across all platforms. With hundreds of optimizations, especially for iOS, Android and Nintendo Switch — which have been built for Fortnite and are now rolled into Unreal Engine 4.20 and released to all users — Epic is providing developers with the scalable tools they need for these types of projects.

Artists working in visual effects, animation, broadcast and virtual production will find enhancements for digital humans, VFX and cinematic depth of field, allowing them to create realistic images across all forms of media and entertainment. In the enterprise space, Unreal Studio 4.20 includes upgrades to the UE4 Datasmith plugin suite, such as SketchUp support, which make it easier to get CAD data prepped, imported and working in Unreal Engine.

Here are some key features of Unreal Engine 4.20:

A new proxy LOD system: Users can handle sprawling worlds via UE4’s production-ready Proxy LOD system for the easy reduction of rendering cost due to poly count, draw calls and material complexity. Proxy LOD offers big gains when developing for mobile and console platforms.

A smoother mobile experience: Over 100 mobile optimizations developed for Fortnite come to all 4.20 users, marking a major shift for easy “shippability” and seamless gameplay optimization across platforms. Major enhancements include improved Android debugging, mobile Landscape improvements, RHI thread on Android and occlusion queries on mobile.

Works better with Switch: Epic has improved Nintendo Switch development by releasing tons of performance and memory improvements built for Fortnite on Nintendo Switch to 4.20 users as well.

Niagara VFX (early access): Unreal Engine’s new programmable VFX editor, Niagara, is now available in early access and will help developers take their VFX to the next level. This new suite of tools is built from the ground up to give artists unprecedented control over particle simulation, rendering and performance for more sophisticated visuals. This tool will eventually replace the Unreal Cascade particle editor.

Cinematic depth of field: Unreal Engine 4.20 delivers tools for achieving depth of field at true cinematic quality in any scene. This brand-new implementation replaces the Circle DOF method. It’s faster, cleaner and provides a cinematic appearance through the use of a procedural bokeh simulation. Cinematic DOF also supports alpha channel and dynamic resolution stability, and has multiple settings for scaling up or down on console platforms based on project requirements. This feature debuted at GDC this year as part of the Star Wars “Reflections” demo by Epic, ILMxLAB and Nvidia.

Digital human improvements: In-engine tools now include dual-lobe specular/double Beckman specular models, backscatter transmission in lights, boundary bleed color subsurface scattering, iris normal slot for eyes and screen space irradiance to build the most cutting-edge digital humans in games and beyond.

Live record and replay: All developers now have access to code from Epic’s Fortnite Replay system. Content creators can easily use footage of recorded gameplay sessions to create incredible replay videos.

Sequencer cinematic updates: New features include frame accuracy, media tracking, curve editor/evaluation and Final Cut Pro 7 XML import/export.

Shotgun integration: Shotgun, a production management and asset tracking solution, is now supported. This will streamline workflows for Shotgun users in game development who are leveraging Unreal’s realtime performance. Shotgun users can assign tasks to specific assets within Unreal Engine.

Mixed reality capture support (early access): Users with virtual production workflows will now have mixed reality capture support that includes video input, calibration and in-game compositing. Supported webcams and HDMI capture devices allow users to pull real-world greenscreened video into the engine, and supported tracking devices can match your camera location to the in-game camera for more dynamic shots.

AR support: Unreal Engine 4.20 ships with native support for ARKit 2, which includes features for creating shared, collaborative AR experiences. Also included is the latest support for Magic Leap One, Google ARCore 1.2 support.

Metadata control: Import metadata from 3ds Max, SketchUp and other common CAD tools for the opportunity to batch process objects by property, or expose metadata via scripts. Metadata enables more creative uses of Unreal Studio, such as Python script commands for updating all meshes of a certain type, or displaying relevant information in interactive experiences.

Mesh editing tools: Unreal Engine now includes a basic mesh editing toolset for quick, simple fixes to imported geometry without having to fix them in the source package and re-import. These tools are ideal for simple touch-ups without having to go to another application. Datasmith also now includes a base Python script that can generate Level of Detail (LOD) meshes automatically.

Non-destructive re-import: Achieve faster iteration through the new parameter tracking system, which monitors updates in both the source data and Unreal Editor, and only imports changed elements. Previous changes to the scene within Unreal Editor are retained and reapplied when source data updates.

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.

Axle Video rebrands as Axle AI

Media management company Axle Video has rebranded as Axle AI. The company has also launched their new Axle AI software, allowing users to automatically index and search large amounts of video, image and audio content.

Axle AI is available either as software, which runs on standard Mac hardware, or as a self-contained software/hardware appliance. Both options provide integrations with leading cloud AI engines. The appliance also includes embedded processing power that supports direct visual search for thousands of hours of footage with no cloud connectivity required. Axle AI has an open architecture, so new third-party capabilities can be added at any time.

Axle has also launched Axle Media Cloud with Wasabi, a 100% cloud-based option for simple media management. The offering is available now and is priced at $400 per month for 10 terabytes of managed storage, 10 user accounts and up to 10 terabytes of downloaded media per month.

In addition, Axle Embedded is a new version of axle software that can be run directly on storage solutions from a range of industry partners, including, G-Technology and Panasas. As with Axle Media Cloud, all of Axle AI’s automated tagging and search capabilities are simple add-ons to the system.

What was new at GTC 2017

By Mike McCarthy

I, once again, had the opportunity to attend Nvidia’s GPU Technology Conference (GTC) in San Jose last week. The event has become much more focused on AI supercomputing and deep learning as those industries mature, but there was also a concentration on VR for those of us from the visual world.

The big news was that Nvidia released the details of its next-generation GPU architecture, code named Volta. The flagship chip will be the Tesla V100 with 5,120 CUDA cores and 15 Teraflops of computing power. It is a huge 815mm chip, created with a 12nm manufacturing process for better energy efficiency. Most of its unique architectural improvements are focused on AI and deep learning with specialized execution units for Tensor calculations, which are foundational to those processes.

Tesla V100

Similar to last year’s GP100, the new Volta chip will initially be available in Nvidia’s SXM2 form factor for dedicated GPU servers like their DGX1, which uses the NVLink bus, now running at 300GB/s. The new GPUs will be a direct swap-in replacement for the current Pascal based GP100 chips. There will also be a 150W version of the chip on a PCIe card similar to their existing Tesla lineup, but only requiring a single half-length slot.

Assuming that Nvidia puts similar processing cores into their next generation of graphics cards, we should be looking at a 33% increase in maximum performance at the top end. The intermediate stages are more difficult to predict, since that depends on how they choose to tier their cards. But the increased efficiency should allow more significant increases in performance for laptops, within existing thermal limitations.

Nvidia is continuing its pursuit of GPU-enabled autonomous cars with its DrivePX2 and Xavier systems for vehicles. The newest version will have a 512 Core Volta GPU and a dedicated deep learning accelerator chip that they are going to open source for other devices. They are targeting larger vehicles now, specifically in the trucking industry this year, with an AI-enabled semi-truck in their booth.

They also had a tractor showing off Blue River’s AI-enabled spraying rig, targeting individual plants for fertilizer or herbicide. It seems like farm equipment would be an optimal place to implement autonomous driving, allowing perfectly straight rows and smooth grades, all in a flat controlled environment with few pedestrians or other dynamic obstructions to be concerned about (think Interstellar). But I didn’t see any reference to them looking in that direction, even with a giant tractor in their AI booth.

On the software and application front, software company SAP showed an interesting implementation of deep learning that analyzes broadcast footage and other content looking to identify logos and branding, in order to provide quantifiable measurements of the effectiveness of various forms of brand advertising. I expect we will continue to see more machine learning implementations of video analysis, for things like automated captioning and descriptive video tracks, as AI becomes more mature.

Nvidia also released an “AI-enabled” version of I-Ray to use image prediction to increase the speed of interactive ray tracing renders. I am hopeful that similar technology could be used to effectively increase the resolution of video footage as well. Basically, a computer sees a low-res image of a car and says, “I know what that car should look like,” and fills in the rest of the visual data. The possibilities are pretty incredible, especially in regard to VFX.

Iray AI

On the VR front, Nvidia announced a new SDK that allows live GPU-accelerated image stitching for stereoscopic VR processing and streaming. It scales from HD to 5K output, splitting the workload across one to four GPUs. The stereoscopic version is doing much more than basic stitching, processing for depth information and using that to filter the output to remove visual anomalies and improve the perception of depth. The output was much cleaner than any other live solution I have seen.

I also got to try my first VR experience recorded with a Light Field camera. This not only gives the user a 360 stereo look around capability, but also the ability to move their head around to shift their perspective within a limited range (based on the size the recording array). The project they were using to demo the technology didn’t highlight the amazing results until the very end of the piece, but when it did that was the most impressive VR implementation I have had the opportunity to experience yet.
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Mike McCarthy is an online editor/workflow consultant with 10 years of experience on feature films and commercials. He has been working on new solutions for tapeless workflows, DSLR filmmaking and multi-screen and surround video experiences. Check out his site.

Nvidia’s GTC 2016: VR, A.I. and self driving cars, oh my!

By Mike McCarthy

Last week, I had the opportunity to attend Nvidia’s GPU Technology Conference, GTC 2016. Five thousand people filled the San Jose Convention Center for nearly a week to learn about GPU technology and how to use it to change our world. GPUs were originally designed to process graphics (hence the name), but are now used to accelerate all sorts of other computational tasks.

The current focus of GPU computing is in three areas:

Virtual reality is a logical extension of the original graphics processing design. VR requires high frame rates with low latency to keep up with user’s head movements, otherwise the lag results in motion sickness. This requires lots of processing power, and the imminent release of the Oculus Rift and HTC Vive head-mounted displays are sure to sell many high-end graphics cards. The new Quadro M6000 24GB PCIe card and M5500 mobile GPU have been released to meet this need.

Autonomous vehicles are being developed that will slowly replace many or all of the driver’s current roles in operating a vehicle. This requires processing lots of sensor input data and making decisions in realtime based on inferences made from that information. Nvidia has developed a number of hardware solutions to meet these needs, with the Drive PX and Drive PX2 expected to be the hardware platform that many car manufacturers rely on to meet those processing needs.

This author calls the Tesla P100 "a monster of a chip."

This author calls the Tesla P100 “a monster of a chip.”

Artificial Intelligence has made significant leaps recently, and the need to process large data sets has grown exponentially. To that end, Nvidia has focused their newest chip development — not on graphics, at least initially — on a deep learning super computer chip. The first Pascal generation GPU, the Tesla P100 is a monster of a chip, with 15 billion 16nm transistors on a 600mm2 die. It should be twice as fast as current options for most tasks, and even more for double precision work and/or large data sets. The chip is initially available in the new DGX-1 supercomputer for $129K, which includes eight of the new GPUs connected in NVLink. I am looking forward to seeing the same graphics processing technology on a PCIe-based Quadro card at some point in the future.

While those three applications for GPU computing all had dedicated hardware released for them, Nvidia has also been working to make sure that software will be developed that uses the level of processing power they can now offer users. To that end, there are all sorts of SDKs and libraries they have been releasing to help developers harness the power of the hardware that is now available. For VR, they have Iray VR, which is a raytracing toolset for creating photorealistic VR experiences, and Iray VR Lite, which allows users to create still renderings to be previewed with HMD displays. They also have a broader VRWorks collection of tools for helping software developers adapt their work for VR experiences. For Autonomous vehicles they have developed libraries of tools for mapping, sensor image analysis, and a deep-learning decision-making neural net for driving called DaveNet. For A.I. computing, cuDNN is for accelerating emerging deep-learning neural networks, running on GPU clusters and supercomputing systems like the new DGX-1.

What Does This Mean for Post Production?
So from a post perspective (ha!), what does this all mean for the future of post production? First, newer and faster GPUs are coming, even if they are not here yet. Much farther off, deep-learning networks may someday log and index all of your footage for you. But the biggest change coming down the pipeline is virtual reality, led by the upcoming commercially available head-mounted displays (HMD). Gaming will drive HMDs into the hands of consumers, and HMDs in the hand of consumers will drive demand for a new type of experience for story-telling, advertising and expression.

As I see it, VR can be created in a variety of continually more immersive steps. The starting point is the HMD, placing the viewer into an isolated and large feeling environment. Existing flat video or stereoscopic content can be viewed without large screens, requiring only minimal processing to format the image for the HMD. The next step is a big jump — when we begin to support head tracking — to allow the viewer to control the direction that they are viewing. This is where we begin to see changes required at all stages of the content production and post pipeline. Scenes need to be created and filmed at 360 degrees.

At the conference, this high-fidelity VR simulation that uses scientifically accurate satellite imagery and data from NASA was shown.

The cameras required to capture 360 degrees of imagery produce a series of video streams that need to be stitched together into a single image, and that image needs to be edited and processed. Then the entire image is made available to the viewer, who then chooses which angle they want to view as it is played. This can be done as a flatten image sphere or, with more source data and processing, as a stereoscopic experience. The user can control the angle they view the scene from, but not the location they are viewing from, which was dictated by the physical placement of the 360-camera system. Video-Stitch just released a new all-in-one package for capturing, recording and streaming 360 video called the Orah 4i, which may make that format more accessible to consumers.

Allowing the user to fully control their perspective and move around within a scene is what makes true VR so unique, but is also much more challenging to create content for. All viewed images must be rendered on the fly, based on input from the user’s motion and position. These renders require all content to exist in 3D space, for the perspective to be generated correctly. While this is nearly impossible for traditional camera footage, it is purely a render challenge for animated content — rendering that used to take weeks must be done in realtime, and at much higher frame rates to keep up with user movement.

For any camera image, depth information is required, which is possible to estimate with calculations based on motion, but not with the level of accuracy required. Instead, if many angles are recorded simultaneously, a 3D analysis of the combination can generate a 3D version of the scene. This is already being done in limited cases for advance VFX work, but it would require taking it to a whole new level. For static content, a 3D model can be created by processing lots of still images, but storytelling will require 3D motion within this environment. This all seems pretty far out there for a traditional post workflow, but there is one case that will lend itself to this format.

Motion capture-based productions already have the 3D data required to render VR perspectives, because VR is the same basic concept as motion tracking cinematography, except that the viewer controls the “camera” instead of the director. We are already seeing photorealistic motion capture movies showing up in theaters, so these are probably the first types of productions that will make the shift to producing full VR content.

The Maxwell Kepler family of cards.

Viewing this content is still a challenge, where again Nvidia GPUs are used on the consumer end. Any VR viewing requires sensor input to track the viewer, which much be processed, and the resulting image must be rendered, usually twice for stereo viewing. This requires a significant level of processing power, so Nvidia has created two tiers of hardware recommendations to ensure that users can get a quality VR experience. For consumers, the VR-Ready program includes complete systems based on the GeForce 970 or higher GPUs, which meet the requirements for comfortable VR viewing. VR-Ready for Professionals is a similar program for the Quadro line, including the M5000 and higher GPUs, included in complete systems from partner ISVs. Currently, MSI’s new WT72 laptop with the new M5500 GPU is the only mobile platform certified VR Ready for Pros. The new mobile Quadro M5500 has the same system architecture as the desktop workstation Quadro M5000, with all 2048 CUDA cores and 8GB RAM.

While the new top-end Maxwell-based Quadro GPUs are exciting, I am really looking forward to seeing Nvidia’s Pascal technology used for graphics processing in the near future. In the meantime, we have enough performance with existing systems to start processing 360-degree videos and VR experiences.

Mike McCarthy is a freelance post engineer and media workflow consultant based in Northern California. He shares his 10 years of technology experience on www.hd4pc.com, and he can be reached at mike@hd4pc.com.