Athenascope
Auto Added by WPeMatico
3 methodologies for automated video game highlight detection and capture
With the rise of livestreaming, gaming has evolved from a toy-like consumer product to a legitimate platform and medium in its own right for entertainment and competition.
Twitch’s viewer base alone has grown from 250,000 average concurrent viewers to over 3 million since its acquisition by Amazon in 2014. Competitors like Facebook Gaming and YouTube Live are following similar trajectories.
The boom in viewership has fueled an ecosystem of supporting products as today’s professional streamers push technology to its limit to increase the production value of their content and automate repetitive aspects of the video production cycle.
The largest streamers hire teams of video editors and social media managers, but growing and part-time streamers struggle to do this themselves or come up with the money to outsource it.
The online streaming game is a grind, with full-time creators putting in eight- if not 12-hour performances on a daily basis. In a bid to capture valuable viewer attention, 24-hour marathon streams are not uncommon either.
However, these hours in front of the camera and keyboard are only half of the streaming grind. Maintaining a constant presence on social media and YouTube fuels the growth of the stream channel and attracts more viewers to catch a stream live, where they may purchase monthly subscriptions, donate and watch ads.
Distilling the most impactful five to 10 minutes of content out of eight or more hours of raw video becomes a non-trivial time commitment. At the top of the food chain, the largest streamers can hire teams of video editors and social media managers to tackle this part of the job, but growing and part-time streamers struggle to find the time to do this themselves or come up with the money to outsource it. There aren’t enough minutes in the day to carefully review all the footage on top of other life and work priorities.
Computer vision analysis of game UI
An emerging solution is to use automated tools to identify key moments in a longer broadcast. Several startups compete to dominate this emerging niche. Differences in their approaches to solving this problem are what differentiate competing solutions from each other. Many of these approaches follow a classic computer science hardware-versus-software dichotomy.
Athenascope was one of the first companies to execute on this concept at scale. Backed by $2.5 million of venture capital funding and an impressive team of Silicon Valley Big Tech alumni, Athenascope developed a computer vision system to identify highlight clips within longer recordings.
In principle, it’s not so different from how self-driving cars operate, but instead of using cameras to read nearby road signs and traffic lights, the tool captures the gamer’s screen and recognizes indicators in the game’s user interface that communicate important events happening in-game: kills and deaths, goals and saves, wins and losses.
These are the same visual cues that traditionally inform the game’s player what is happening in the game. In modern game UIs, this information is high-contrast, clear and unobscured, and typically located in predictable, fixed locations on the screen at all times. This predictability and clarity lends itself extremely well to computer vision techniques such as optical character recognition (OCR) — reading text from an image.
The stakes here are lower than self-driving cars, too, since a false positive from this system produces nothing more than a less-exciting-than-average video clip — not a car crash.
Powered by WPeMatico
Athenascope nabs $2.5M seed led by First Round to bring gamers AI-edited highlight reels
As massive cross-platform gaming titles become even larger time-sucks for a lot of people, it’s probably worth reflecting on how to savor your in-game accomplishments.
Streaming of esports celebrities on sites like Twitch has taken off like no one imagined, but for the most part the toil-heavy editing process has left this attention largely focused on those with the ambitions of making gaming their full-time gig.
Athenascope is a small startup aiming to tap computer vision intelligence to record, review and recap what more novice gamers were able to pull off in their latest battle royale with a short, shareable highlight reel. The team is led by Chris Kirmse, who previously founded Xfire, a game messaging client that Viacom bought in 2006 for north of $100 million.
The company announced this week that they’ve closed a $2.5 million seed round led by First Round Capital to grow its tools and its team. They’re also rolling out their AI highlight reel tool for gamers. The tool is pretty customized for individual titles; they’re launching with support for Fortnite, Rocket League and PUBG, but Kirmse hopes to expand that list significantly in the future.
Josh Kopelman, a partner at First Round Capital who is joining Athenascope’s board, highlighted that a lot of existing tools for gaming entertainment are “really skewed towards the high-end.”
“They’re not democratized, they’re for professional gamers,” Kopelman told TechCrunch. “What I think Chris is trying to do with Athenascope is enable anyone to create these high-quality game highlights — what the pros have to do manually.”
The company is tackling a problem familiar to video-editing software companies: how to prevent footage from dying on the device. The answer here is the same as many others have posited, tapping computer vision deep learning to do the heavy lifting in determining which footage is interesting and worthy of a highlight reel. Athenascope has some key advantages over the companies like GoPro that are trying to do the same with real-world video, namely the games they support operate in fundamentally more predictable ways and 2D interface cues offer some pretty healthy indicators of when exciting stuff is going down.
The game isn’t a plug-in that needs pipeline access to your Fortnite account or anything, the product simply analyzes exactly what you’re seeing when you play. The startup is also working on cool tools that allow you to see multiple perspectives of individual moments in gameplay by essentially syncing footage from other people involved in a match that are also Athenascope’s service and giving a sort of multi-view replay.
The company has broader ambitions of how it can evolve these gaming insights with computer vision, including ways to help gamers learn about their strengths and weaknesses in a way that lets Athenascope serve as a sort of computer vision coach. For now though, the big focus is on getting gamers these entertaining snapshots of their gaming experiences in an intelligent way.
Powered by WPeMatico